42.2.1 Causes of HL

There are a variety of ways in which HL can occur (World Health Organization, 2018). Causes include exposure to very loud sounds, physical trauma, hereditary syndromes, side effects of medications and other treatments, infection, chronic illnesses (e.g., diabetes), and normal ageing. Congenital HL, present at birth, may be caused by genetic variation, complications during birth, or viral infection, among other factors. Although HL can stem from problems with the outer and middle ear (e.g., a perforated eardrum), many of the aforementioned conditions affect the inner ear wherein cochlear hair cells, the sensory receptors of the auditory system, are damaged. HL can also arise from issues affecting the auditory nerve (Kaga, Reference Kaga2016). HL can advance very gradually, especially for age-related HL, meaning that someone may not notice a difference until their HL can be characterized as moderate or poorer. At this point, changes in central auditory processing, including cognitive and behavioral adaptations to cope with the reduced or degraded sensory input, have likely already occurred (Herrmann and Butler, Reference Herrmann and Butler2021; Mitchell and Maslin, Reference Mitchell and Maslin2007; Slade et al., Reference Slade, Plack and Nuttall2020).

42.2.2 Hearing Devices

Whether HL is treatable depends on the underlying cause, but cochlear damage is usually irreversible. In the case of mild to moderate permanent HL, a listener can make use of a hearing aid (HA) to amplify the acoustic signal and compensate the audibility of specific frequencies; however, when HL is severe to profound, a CI may help to restore a sense of hearing. A CI is an auditory prosthesis that bypasses the inner ear (Carlyon and Goehring, Reference Carlyon and Goehring2021; Fan-Gang Zeng et al., Reference Zeng, Rebscher, Harrison, Sun and Feng2008; Loizou, Reference Loizou1999). It consists of an array of electrodes surgically implanted within a person’s cochlea, which receive a signal originating from the external components of the system. Briefly, the CI pipeline from sound source to auditory percept is as follows: First, sounds in the environment are sampled by an external microphone (or multiple microphones). An external processor then divides this signal into its component frequencies, emulating the frequency selectivity of the inner ear. Other signal processing techniques, such as noise reduction, may also be implemented at this step. The processed signal, now converted into discretized pulses, is transmitted via radio waves to an internal receiver, which conveys the signal onward to the stimulator implanted within the cochlea. Here, each signal component activates a specific electrode, thereby directly interfacing with the auditory nerve. Whereas acoustic hearing entails thousands of individual cochlear hair cells, CIs normally contain around 22 individual electrodes or fewer – in practice, the number of useable electrodes is often even lower, depending on the health of the auditory nerve and/or interaction between channels due to electrical current spread within the cochlea (Goehring et al., Reference Goehring, Archer-Boyd, Arenberg and Carlyon2021). Despite limited spectro-temporal resolution, however, many people with CIs have excellent speech reception in quiet, and some are furthermore able to communicate via voice calls in the complete absence of nonauditory cues (e.g., lip-reading). CI listening should be considered as a perceptual representation of sound that is distinct from typical acoustic hearing, and in particular, CI users who received their implant later in life usually require time, patience, effort, and support to make the most of their device (Boisvert et al., Reference Boisvert, Reis, Au, Cowan and Dowell2020; Tang et al., Reference Tang, Thompson and Clark2017).

42.3.1 Influences of HL at the Auditory Periphery

Even when accounting for differences in audibility and perceptual thresholds, people with moderate to severe HL may also experience other changes regarding their sensitivity to, and processing of, sounds. For example, people with HL show reduced frequency selectivity (Moore, Reference Moore1985, Reference Moore1996). This increases the threshold at which contrasts in spectral quality are perceived, which can affect the discrimination of vowel sounds. HL is also associated with poorer dynamic range, possibly due to a reduction in the compression performed at the basilar membrane, caused by damage or loss of the outer hair cells that would normally selectively amplify vibration and sharpen frequency tuning (Moore, Reference Moore1996). The consequence is that softer sounds may become inaudible, while louder ones are perceived similarly to typically hearing listeners – or, in some cases, as intolerably loud. This may influence temporal processing relatively early in the auditory system. For instance, in silent gap detection tasks, experimental participants with HL require longer gap duration values, in comparison to their typically hearing counterparts, when the stimuli in which the gap occurs consist of narrowband noise (Glasberg and Moore, Reference Glasberg and Moore1992). This effective reduction in temporal resolution may be related to the amplitude fluctuations inherent to noise: With reduced dynamic range, randomly occurring peaks and troughs within the noise amplitude modulation patterns may be perceptually exaggerated, making true gaps more difficult to detect. Another temporal consequence of cochlear damage relates to how certain acoustic features, such as perceived pitch or loudness, are processed over time at the sub-second level. Specifically, for typically hearing listeners, increases in stimulus duration tend to accompany a change in thresholds required to perceive the feature of interest (Kidd et al., Reference Kidd, Mason and Feth1984). For instance, a short tone following the presentation of a longer tone will need to have a relatively higher sound pressure level in order to be perceived as identically loud. This process is referred to as temporal integration, and is theorized to reflect how sensory information is sampled and/or accumulated over time (Florentine et al., Reference Florentine, Fastl and Buus1988). Hence, HL may lead to weaker or less stable representations of sustained sounds at short timescales, which could in turn affect the processing of temporal structure at longer timescales, as in speech or music (Moore, Reference Moore2003).

42.3.2 HL and Central Auditory Processing

At the auditory periphery, the aforementioned HL-related differences in temporal processing operate at the timescale of tens of milliseconds or fewer. These differences could in turn affect speech processing at the timing of syllables or phonemes by potentially distorting or obscuring salient temporal cues (e.g., the roughly 70 milliseconds of voice onset time that distinguish ta from da). However, HL is often accompanied by more central changes that could also impact the perception of speech rhythm at slower timescale, i.e., at the level of words and phrases. In this section, I will survey the empirical literature investigating how people with HL perceive and reproduce temporal patterns and structure in speech with a focus on cognitive and other higher-level aspects of sensory processing. I begin by discussing congenital and early HL, and examine how perception and production of speech rhythm, as well as other aspects of temporal and prosodic processing, develop in children who use CIs and HAs for speech communication. I then address post-lingual HL in adults, before turning to focus on the interaction between HL and cognitive changes associated with normal ageing, especially with relevance to speech in noise perception.

42.4.1 Future Directions for the Rehabilitation of Speech Rhythm in HL

For individuals with HL, engagement with speech rhythm depends on complex, intersecting factors. These factors can include the underlying cause of the HL, its onset relative to language acquisition, and which forms of amplification are used (if any), in addition to numerous other determinants. What is clear is that there is no singular experience of HL. As this chapter has discussed, HL may affect the perception of speech timing from the inner ear to the cortex, and individual differences in sensitivity to temporal cues and rhythmic structure could also help to explain why outcomes in functional hearing differ so much across listeners, irrespective of audiometry. Finally, HL can be further complicated by ageing, which is accompanied by cascading changes throughout the central nervous system that also affect temporal processing. In general, the empirical evidence surveyed in this chapter suggests that many people with HL encounter difficulties involving the perception and production of speech rhythm, but this is unlikely to be caused by the complete absence of timing information, particularly for listeners with CIs. Rather, in accordance with the literature examining musical rhythm perception, it appears that the greatest challenges for people with HL arise under ecological conditions, namely, when temporal patterning and structure represent just one facet of a multidimensional, sensorially rich percept – take, for example, multi-party conversational speech in a busy café. Hence, it is crucial that researchers make every effort to incorporate realism into experimental stimuli moving forward.

43.2.1 Effectiveness Evidence: Does It Work?

For decades after MIT was developed, the research studies evaluating its effectiveness were virtually all case studies or case series. Some of the early studies identified factors that differentiated treatment responders from nonresponders, showing improvement in at least some of the participants (Naeser and Helm-Estabrooks, Reference Naeser and Helm-Estabrooks1985; Sparks et al., Reference Sparks, Helm and Albert1974). Later small-sample and case studies continued to show treatment effects of MIT, demonstrating effects on trained utterances, generalized improvement on standardized measures of language function, or generalization to propositional speech tasks such as conversation or picture description (e.g., Curtis et al., Reference Curtis, Nicholas, Pittmann and Zipse2020; Hough, Reference Hough2010; Wilson et al., Reference Wilson, Parsons and Reutens2006; Zipse et al., Reference Zipse, Norton, Marchina and Schlaug2012; Zumbansen et al., Reference Zumbansen, Peretz and Hébert2014b). However, these studies were often not well controlled, and some were conducted in the subacute phase, when spontaneous neurological recovery was likely a contributing factor to the observed improvements (Popescu et al., Reference Popescu, Stahl and Wiernik2022).

In more recent years, research studies considered to be higher-level evidence, including RCTs, systematic reviews, and meta-analyses, have addressed the efficacy of MIT. Van der Meulen et al. conducted two RCTs including participants with severe nonfluent aphasia, the first with PWAs in the subacute phase and the second with PWAs in the chronic phase (Reference Van der Meulen, Van De Sandt-Koenderman, Heijenbrok, Visch-Brink and Ribbers2014, Reference Van der Meulen, Van De Sandt-Koenderman, Heijenbrok, Visch-Brink and Ribbers2016). In the subacute phase, PWAs treated with MIT showed significantly more improvement on repetition of both trained and untrained utterances compared to deferred-treatment control participants, and they also showed evidence of better generalization to functional communication. In the chronic phase, the participants treated with MIT showed more improvement on trained utterances than the no-treatment control group but did not show robust evidence of generalization to other utterances or tasks, and gains were not maintained at a six-week follow-up. In a systematic review of four RCTs, Haro-Martínez et al. (Reference Haro-Martínez, Pérez-Araujo, Sanchez-Caro, Fuentes and Díez-Tejedor2021) found that MIT results in improved repetition but not auditory comprehension, and there is less robust evidence that it leads to improved functional communication. In the most comprehensive meta-analysis to date, Popescu et al. (Reference Popescu, Stahl and Wiernik2022) considered both group-level data from RCTs as well as individual participant data from case reports. They found that MIT produces small positive effects, mostly on repetition tasks, with greater effects for trained than untrained utterances. The authors concluded that MIT alone may not substantially improve everyday communication.

In summary, the evidence for MIT is rather modest. However, a noteworthy finding from the analysis conducted by Popescu et al. (Reference Popescu, Stahl and Wiernik2022) is that the treatment effect sizes are substantially larger for single-subject design studies than for RCTs, and effect sizes are larger for modified treatment protocols compared to the original protocol. The authors very reasonably interpret this as illustrating the importance of well-controlled studies to account for spontaneous recovery and other factors. Another possible interpretation, though, is that these results highlight the importance of adjusting the MIT protocol to the individual PWA. In contrast to the more rigid protocols typically used in RCTs, single-subject designs allow more flexibility for the clinician to respond to the behavior and preferences of the clients, as in clinical practice. This responsiveness is likely critical when treating complex behavior in a heterogeneous population.

43.2.2 Target Population: For Whom Does It Work?

Aphasia varies widely across individuals in terms of subtype, severity, etiology, and time post-onset. Other comorbid conditions also affect how aphasia is managed and which treatments are considered appropriate. Although there has not been extensive research on the factors associated with a meaningful treatment response to MIT, good treatment candidates are considered to be PWAs with a single left-hemisphere lesion resulting in nonfluent aphasia with severely limited verbal output, poor repetition even at the single-word level, relatively good auditory comprehension, poor articulation, good emotional stability, and adequate attentional capacity (American Academy of Neurology, 1994; Sparks, Reference Sparks and Chapey2008). Consistent with this, most treatment studies have focused on people with nonfluent aphasia, particularly moderate to severe Broca’s aphasia, though a minority of studies have included a wider variety of PWAs (García-Casares et al., Reference García-Casares, Barros-Cano and García-Arnés2022; Haro-Martínez et al., Reference Haro-Martínez, Pérez-Araujo, Sanchez-Caro, Fuentes and Díez-Tejedor2021). However, the response to MIT is variable even across individuals who fit the treatment criteria (Van der Meulen et al., Reference Van der Meulen, Van De Sandt-Koenderman, Heijenbrok, Visch-Brink and Ribbers2016).

One critical variable is whether a PWA also has apraxia of speech (AOS), a motor speech-planning disorder that manifests in slow, distorted speech with atypical prosody, and sometimes audible or visible articulatory groping. In contrast to aphasia, a disorder of language, AOS affects the planning and programming of the motor commands for speech articulation. While this is a reasonably clear distinction in theory, in practice the two conditions can be challenging to differentiate, particularly when aphasia results in phonemic errors, and the conditions often co-occur (Basilakos et al., Reference Basilakos, Yourganov and Den Ouden2017; Strand et al., Reference Strand, Duffy, Clark and Josephs2014). The developers of MIT initially reported that good responders exhibited “restricted but clearly articulated stereotype-like speech,” indicating that these initial patients may not have had AOS (Sparks et al., Reference Sparks, Helm and Albert1974, p. 312). The researchers did note, though, that this particular criterion should be interpreted with caution due to the small sample size in their study, and the treatment criteria have evolved over time to specify that the best candidates show “diminished articulatory agility and effortful initiation of speech production” – a description that applies to AOS (American Academy of Neurology, 1994, p. 566). It is noteworthy that at the time MIT was developed, “apraxia of speech” was a new term, coined by Darley et al., and AOS was not widely accepted as a disorder separate from aphasia (Darley et al., Reference Darley, Aronson and Brown1969). It therefore was not considered as a factor for treatment eligibility.

To date, most studies evaluating music-based treatments for aphasia more broadly include PWAs with a concomitant motor speech disorder, usually AOS (Zumbansen and Tremblay, Reference Zumbansen and Tremblay2019). In fact, the odds of a music-based intervention resulting in improved speech outcomes is approximately 21 times higher, and the odds of the treatment resulting in improved language outcomes about four times higher, in people with aphasia and a motor speech disorder compared to those with aphasia alone (Zumbansen and Tremblay, Reference Zumbansen and Tremblay2019). Furthermore, many of the elements included in MIT are commonly used in treatments for AOS, including slow rate, regular rhythm, and tapping or other means to enact pacing (Ballard et al., Reference Ballard, Wambaugh and Duffy2015; Wambaugh et al., Reference Wambaugh and Martinez2000). Adaptations of the standard MIT protocol, even when being used with the rationale of treating aphasia, often include additional elements suited to treating AOS, such as articulatory-kinematic approaches and/or the use of automated phrases to induce a correct production of a target word (e.g., Zhang et al., Reference Zhang, Yu and Teng2021). It seems, then, that having both AOS and aphasia may make one a particularly good candidate for MIT, although this depends on how exactly the treatment is used.

43.2.3 Outcome Measures: What Does “Working” Mean?

In addition to determining individual patient factors, it is also important to consider what “working” means. Across the literature, MIT has been used with different treatment goals and outcome measures. MIT was created as a treatment to stimulate the language system, with the aim of improving spontaneous, propositional language production (Sparks and Holland, Reference Sparks and Holland1976). It has also been used to train specific utterances, with the aim of creating a set of almost automatic phrases the PWA can use in functional ways; Zumbansen et al. have referred to this as “palliative” use (Reference Zumbansen, Peretz and Hébert2014a; also see Section 43.3.8.2). Indeed, a number of research studies have taken this approach, quite likely because it offers advantages in terms of research design and experimental control (e.g., Hough, Reference Hough2010; Stahl et al., Reference Stahl, Henseler, Turner, Geyer and Kotz2013). A third approach is to teach MIT as a strategy: The music-based elements are taught as a facilitation technique the PWA can use when they experience difficulty in everyday communication (see Zumbansen et al., Reference Zumbansen, Peretz and Hébert2014a, for an in-depth consideration of different treatment goals). This third approach is used in a French adaptation of MIT, thérapie mélodique et rhythmée (TMR) (Van Eeckhout and Bhatt, Reference Van Eeckhout and Bhatt1984).

These different treatment goals are reflected in different outcome measures (Van der Meulen et al., Reference Van der Meulen, Van De Sandt-Koenderman, Heijenbrok, Visch-Brink and Ribbers2016). For example, if MIT is being used to improve propositional language or as a compensatory technique to circumvent difficulties, outcomes should be evaluated with a measure that reflects improvement of functional communication. This may include analyses of language samples (using correct information units, CIUs, or similar, as in Schlaug et al., Reference Schlaug, Marchina and Norton2008) or self or caregiver report of functional changes. Alternatively or in addition, some studies have used standard language measures to evaluate improvement in naming, repetition, auditory comprehension, or other core language functions (e.g., Belin et al., Reference Belin, Van Eeckhout and Zilbovicius1996; Sparks et al., Reference Sparks, Helm and Albert1974). An important consideration is that standardized language assessments are typically developed to diagnose and classify aphasia rather than to document change, and they may not offer adequate sensitivity for this latter purpose. For studies evaluating MIT for palliative use, performance on trained phrases may be the primary outcome measure. Finally, some studies use measures at multiple levels – repetition of trained phrases, repetition of untrained phrases, and measures of functional language use (e.g., Curtis et al., Reference Curtis, Nicholas, Pittmann and Zipse2020; Zipse et al., Reference Zipse, Norton, Marchina and Schlaug2012). This last approach is the most thorough for documenting improvement at any level and evaluating generalization.

43.2.4 Treatment Theory: How Does It Work?

The developers of MIT proposed that exaggerated speech melody and left-hand tapping would engage the right hemisphere, since the right hemisphere was viewed as responsible for processing music and prosody, and since left-hand tapping requires activation of right-hemisphere motor areas. The right-hemisphere engagement would reduce reliance on the lesioned left hemisphere, but ultimately support recovery of left-hemisphere control of spoken language (Albert et al., Reference Albert, Sparks and Helm1973; Helm-Estabrooks and Albert, Reference Helm-Estabrooks and Albert2004; Sparks et al., Reference Sparks, Helm and Albert1974). This idea was bolstered by an early finding that good responders to MIT did not have right-hemisphere lesions, while poor responders sometimes did (Naeser and Helm-Estabrooks, Reference Naeser and Helm-Estabrooks1985).

More recently, it has been debated quite extensively in the literature whether neuroplastic changes in the right or the left hemisphere are correlated with MIT-induced behavioral changes. There is evidence for both structural and functional changes in the right hemisphere of PWAs who have responded to MIT, which the researchers interpreted as lending support to the idea that MIT spurs increased right-hemisphere involvement in spoken language production (e.g., Schlaug et al., Reference Schlaug, Marchina and Norton2008; Tabei et al., Reference Tabei, Satoh and Nakano2016; Wan et al., Reference Wan, Zheng, Marchina, Norton and Schlaug2014). However, other neuroimaging studies have found no increase in right-hemisphere activation and an increase in left-hemisphere activation, especially perilesional areas, during repetition of phrases using MIT techniques (Belin et al., Reference Belin, Van Eeckhout and Zilbovicius1996; Breier et al., Reference Breier, Randle, Maher and Papanicolaou2010; Laine et al., Reference Laine, Tuomainen and Ahonen1994). This has been viewed as evidence that spared left-hemisphere tissue supports MIT’s treatment effect. A recent systematic review concluded that the neural underpinnings of MIT-based improvement are complex and varied across individuals (García-Casares et al., Reference García-Casares, Barros-Cano and García-Arnés2022). It seems likely that neuroplastic changes are determined in part by how much left hemisphere is spared. If enough left-hemisphere tissue remains, the perilesional tissue may support MIT-mediated recovery, while if the lesion is very large, homologous right-hemisphere areas may offer the only potential for recovery (Wan et al., Reference Wan, Zheng, Marchina, Norton and Schlaug2014; Zumbansen et al., Reference Zumbansen, Peretz and Hébert2014a). (The idea that the neural basis of aphasia recovery depends on lesion size has previously been discussed in the context of other treatment approaches, e.g., Crosson et al., Reference Crosson, Moore and Gopinath2005.)

In addition to theories that address neuroplastic reorganization, other mechanisms for MIT-induced language recovery have been suggested (see Merrett et al., Reference Merrett, Peretz and Wilson2014, for an excellent review). These include activation of shared features of language and music, engagement of the mirror neuron system, and improvements to mood and motivation. These various theories are not necessarily distinct; for example, activating musical elements that overlap with aspects of language may drive neuroplastic change, recalling Luria’s idea of intersystemic reorganization (Reference Luria1970), where a damaged neural system is coupled with an intact one to promote improvement of the weaker system. The idea that MIT leverages prosody as a link between music and language is perhaps the most discussed theory, whether framed in terms of neurophysiology or cognitive models.

Within the field of rehabilitation more broadly, there have been efforts in the past decade to specify treatments in terms of their ingredients and how these ingredients effect change. Conceptual models of treatment theory and specification, and their application in clinical practice, are active areas of scholarship with implications for how clinicians conceive of, describe, apply, and measure the outcomes of interventions (Hart et al., Reference Hart, Dijkers and Whyte2019; Whyte et al., Reference Whyte, Dijkers and Hart2014; Zanca et al., Reference Zanca, Turkstra and Chen2019; also see Section 43.4.1). Accordingly, the following section considers the various treatment ingredients of MIT and how each of these may work to facilitate spoken-language production and promote aphasia recovery.

43.3.1 Rhythm

There is growing evidence that rhythm is a particularly important aspect of MIT. Using a beat-based rhythm as a facilitating technique, Stahl et al. (Reference Stahl, Kotz, Henseler, Turner and Geyer2011) concluded that PWAs do not benefit from singing more than from rhythmic speech during a repetition task, while Kershenbaum et al. (Reference Kershenbaum, Nicholas, Hunsaker and Zipse2019) found that rhythmic speech was more facilitating than singing in their sample of PWAs. Rhythm also seems to enhance treatment effects. In a scripted sentence-learning paradigm that shared some features of MIT, Quique et al. (Reference Quique, Evans, Ortega-Llebaría, Zipse and Dickey2022) demonstrated that sentence learning was improved by the addition of rhythmic beats to the audio track (all conditions were spoken, with none sung or intoned). Studies comparing melodic (sung) treatment conditions to rhythmically spoken ones have found comparable gains in trained utterances across the two conditions (Stahl et al., Reference Stahl, Henseler, Turner, Geyer and Kotz2013; Zumbansen et al., Reference Zumbansen, Peretz and Hébert2014b; also see Boucher et al., Reference Boucher, Garcia, Fleurant and Paradis2001, for a related finding). However, generalization to functional communication may be better with melodic than with rhythmically spoken treatment. Zumbansen et al. (Reference Zumbansen, Peretz and Hébert2014b) used a within-subjects design to compare three treatment conditions: melodic (rhythmically intoned, with hand tapping), rhythmically spoken (with hand tapping), and normally spoken. All participants made significant gains on trained phrases with all three treatments, but generalization to functional communication was only found after the melodic treatment.

Of course, “rhythmic” production can be realized in different ways. Early versions of MIT used quarter and eighth notes, respectively, for stressed and unstressed syllables (Sparks, Reference Sparks and Chapey2008). Later descriptions by the originators of MIT specified that productions should follow the “stress and rhythm patterns associated with normal speech” (Helm-Estabrooks and Albert, Reference Helm-Estabrooks and Albert2004, p. 224). TMR, a French version of MIT, uses two durations: longer for naturally stressed syllables and syllables of function words (which tend to be omitted in people with Broca’s aphasia), and shorter for all other syllables (Van Eeckhout and Bhatt, Reference Van Eeckhout and Bhatt1984; Zumbansen et al., Reference Zumbansen, Peretz and Hébert2014b). In contrast, some instantiations of MIT present all syllables with equal duration, lending the utterance a steady, metronomic quality (e.g., Curtis et al., Reference Curtis, Nicholas, Pittmann and Zipse2020). Many studies, though, do not comment on the rhythm used. (See Chapters 30, 33, and 40 for a consideration of how the rhythmic class of a language affects spoken-language learning in speakers without aphasia.)

Even when slightly more complex rhythms are used, the utterances likely adhere to a metrical pattern because each target utterance is repeated multiple times, both within and across steps. For example, the PWA has multiple attempts to intone the utterance in unison with the clinician (unison step). If successful, the PWA moves on to the next step, where they intone in unison with the clinician, who “fades out” and leaves the PWA to complete the utterance on their own (unison fade; Helm-Estabrooks and Albert, Reference Helm-Estabrooks and Albert2004). Across these two steps, the target utterance is typically repeated several times. When a motor action is cyclically repeated, it tends to adhere to a hierarchical timing structure, creating metrical levels (Cummins and Port, Reference Cummins and Port1998; Murton et al., Reference Murton, Zipse, Jacoby and Shattuck-Hufnagel2017).

Rhythmic production, repetition, and the meter that emerges result in expectation. Indeed, London has defined meter as “a stable and recurring pattern of hierarchically structured temporal expectations” (Reference London2002, p. 529). Regular timing of stressed syllables, adhering to a metrical structure, helps listeners predict when these syllables will occur and direct their attention accordingly (Pitt and Samuel, Reference Pitt and Samuel1990). This predictability makes it easier for listeners to entrain to the stimulus, aligning their motor actions – in this case, speech – with an external stimulus. In fact, speech stimuli that follow a simple metrical structure have been shown to facilitate accurate spoken-language production in people with aphasia and/or AOS (Aichert et al., Reference Aichert, Lehner, Falk, Späth and Ziegler2019; Kershenbaum et al., Reference Kershenbaum, Galassi, Shattuck-Hufnagel, Bachan and Zipse2024). At a neurophysiological level, rhythmically regular speech input may promote the entrainment of intrinsic neural oscillatory activity to the speech signal, allowing for the coupling of perception and production (Haegens and Zion Golumbic, Reference Haegens and Zion Golumbic2018; Large and Jones, Reference Large and Jones1999; see Chapters 3, 5, and 6). This may help with the organization of motor commands, perhaps targeting AOS in particular. Notably, treatments using metronome pacing, hand tapping, and other rhythmic elements have been described for AOS (Brendel and Ziegler, Reference Brendel and Ziegler2008; Mauszycki and Wambaugh, Reference Mauszycki and Wambaugh2008). A metrically regular context may also serve to prime words with lexical stress that fits the pattern, aiding retrieval of phonological word forms – an area of impairment across all subtypes of aphasia. This facilitation is analogous to how lyrics are better recalled when sung than when spoken (Kasdan and Kiran, Reference Kasdan and Kiran2018).

43.3.2 Rate

Rhythm is one way in which timing can be manipulated in MIT, and rate is another. The originators of MIT specified that a slow rate should be used (Helm-Estabrooks and Albert, Reference Helm-Estabrooks and Albert2004; Sparks, Reference Sparks and Chapey2008). Singing naturally tends to slow the rate of articulation (Racette, Reference Racette2006; Stahl and Kotz, Reference Stahl and Kotz2014). Laughlin et al. (Reference Laughlin, Naeser and Gordon1979) compared the number of phrases correctly produced during MIT under three conditions: spoken syllables < one second in duration, intoned syllables 1.5 seconds in duration, and intoned syllables two seconds in duration. The best results were observed with the slowest rate, a much slower rate than typically reported in MIT treatment studies. Rates reported in the literature include one second/syllable (Merrett et al., Reference Merrett, Tailby, Jackson and Wilson2019), 750 ms/syllable (Curtis et al., Reference Curtis, Nicholas, Pittmann and Zipse2020), and 600 or 1,200 ms, for unstressed non-functor syllables versus stressed syllables or functors (Zumbansen et al., Reference Zumbansen, Peretz and Hébert2014b). Many studies, however, do not specify the rate used.

A slow rate is helpful because it allows for more processing time for perceiving the model utterance, as well as for planning and executing articulatory commands (Stahl and Kotz, Reference Stahl and Kotz2014). This extra time may be beneficial when the right hemisphere is more involved in controlling spoken-language production, since this hemisphere is thought to operate at a slower timescale than the left (Poeppel, Reference Poeppel2003; Zatorre, Reference Zatorre2001). A slow rate may affect both language processing and speech planning, though the latter may be particularly important: A number of studies that use a slow rate with good effects, either as a treatment or facilitation technique, have outcome measures that evaluate speech accuracy, such as percentage of syllables correct (Boucher et al., Reference Boucher, Garcia, Fleurant and Paradis2001; Kershenbaum et al., Reference Kershenbaum, Nicholas, Hunsaker and Zipse2019; Laughlin et al., Reference Laughlin, Naeser and Gordon1979; Stahl et al., Reference Stahl, Kotz, Henseler, Turner and Geyer2011). Furthermore, along with a regular rhythm, a slowed rate has been shown to increase articulatory accuracy in people with AOS, who typically also have aphasia (Mauszycki and Wambaugh, Reference Mauszycki and Wambaugh2008; Wambaugh and Martinez, Reference Wambaugh and Martinez2000).

43.3.3 Tapping

Another timing-related element of MIT is tapping. In the standard MIT protocol, the clinician sits across from the PWA, with their right hand over the PWA’s left hand, so that the clinician can pick up the patient’s hand and tap it on the tabletop. Protocols differ in whether tapping is once for each syllable, or only on stressed syllables (Helm-Estabrooks and Albert, Reference Helm-Estabrooks and Albert2004; Sparks and Holland, Reference Sparks and Holland1976). As the PWA advances through the MIT levels and learns the procedure, the clinician may discontinue guiding the patient’s tapping and simply monitor that they continue to tap correctly (Sparks, Reference Sparks and Chapey2008). Other variations of MIT (e.g., TMR) allow for tapping with other parts of the body or do not include tapping at all (Zumbansen et al., Reference Zumbansen, Peretz and Hébert2014a). No-tapping variations of MIT were developed in response to observations of individual patients; there is preliminary evidence that tapping may not always be beneficial in MIT, and some PWAs respond better without it (Curtis et al., Reference Curtis, Nicholas, Pittmann and Zipse2020; Hough, Reference Hough2010). As with rate and rhythm, many studies do not provide a description of how tapping was implemented.

Tapping is thought to work by activating motor regions in the right hemisphere. In particular, the cortical representation of the articulators is near regions representing the hand. Tapping may prime a sensorimotor network that integrates perception and action and is useful for producing spoken language (Gentilucci and Dalla Volta, Reference Gentilucci and Dalla Volta2008; Norton et al., Reference Norton, Zipse, Marchina and Schlaug2009; Schlaug et al., Reference Schlaug, Marchina and Norton2008). Related to this, tapping to an isochronous or otherwise predictable rhythm may act to promote pacing, shown to have benefits for motor speech disorders (Brendel and Ziegler, Reference Brendel and Ziegler2008). Furthermore, tapping can be viewed as a simplified gesture, specifically beat gesture, and gestural planning is closely related to prosodic planning (Rohrer et al., Reference Rohrer, Delais-Roussarie and Prieto2023). Emphasizing prosody with gesture may promote spoken-language production.

43.3.4 Pitch Modulation

Pitch modulation has often been viewed as the core feature of MIT (Norton et al., Reference Norton, Zipse, Marchina and Schlaug2009). As noted above, though, the rhythmic elements of the treatment have been more strongly linked to the facilitation associated with MIT-based techniques. Pitch modulation has been used in various ways in different adaptations of MIT, with the most common versions using either two notes or simple melodies. Specified intervals for the two-note variations have included a minor third or a fourth (Norton et al., Reference Norton, Zipse, Marchina and Schlaug2009; Zumbansen et al., Reference Zumbansen, Peretz and Hébert2014b). In English, stressed syllables are produced on the higher pitch and unstressed on the lower, while the dominant French adaptation of MIT, TMR, uses the higher pitch to highlight functor words (typically omitted by people with Broca’s aphasia) as well as stressed syllables (Albert et al., Reference Albert, Sparks and Helm1973; Van Eeckhout and Bhatt, Reference Van Eeckhout and Bhatt1984). Another key consideration related to pitch modulation is whether and how Sprechgesang (“speech song”) is implemented. This technique uses an exaggerated rhythm but speech-like intonation and is introduced in the later stages of MIT (Level III or IV, depending on the protocol used), as a way to transition from intoning to normal speech (Helm-Estabrooks and Albert, Reference Helm-Estabrooks and Albert2004; Sparks, Reference Sparks and Chapey2008; Sparks and Holland, Reference Sparks and Holland1976). Some MIT treatment studies use Sprechgesang (Hough, Reference Hough2010; Van der Meulen et al., Reference Van der Meulen, Van De Sandt-Koenderman, Heijenbrok, Visch-Brink and Ribbers2014, Reference Van der Meulen, Van De Sandt-Koenderman, Heijenbrok, Visch-Brink and Ribbers2016; Zhang et al., Reference Zhang, Yu and Teng2021) while others do not (Curtis et al., Reference Curtis, Nicholas, Pittmann and Zipse2020; Wilson et al., Reference Wilson, Parsons and Reutens2006; Zumbansen et al., Reference Zumbansen, Peretz and Hébert2014b).

Like many of the other elements of MIT, pitch modulation was included to promote right-hemisphere involvement, since the right hemisphere was seen as dominant in processing music and prosody (Sparks et al., Reference Sparks, Helm and Albert1974). Singing has also been noted to slow articulatory rate, as mentioned above (Section 43.3.2), and promote constant voicing across syllables, both of which have been mentioned as potentially important (Helm-Estabrooks and Albert, Reference Helm-Estabrooks and Albert2004; Norton et al., Reference Norton, Zipse, Marchina and Schlaug2009). These manipulations of rate and voicing may primarily affect motor speech rather than language. On the other hand, in modified MIT protocols that include more complex melodies and even instrumental accompaniment, the melody may serve as a mnemonic device to aid in retrieval of the phonological word forms, rather than to facilitate motor speech function (Baker, Reference Baker2000).

43.3.5 Unison Production

Speaking along with another person or a recording, commonly referred to as unison or choral production, is a component of a number of aphasia treatments besides MIT, whether spoken or sung (Kershenbaum et al., Reference Kershenbaum, Galassi, Shattuck-Hufnagel, Bachan and Zipse2024). It is used to promote fluent, accurate productions in other clinical populations, including people who stutter and people with Parkinson’s disease (Juste et al., Reference Juste, Sassi, Costa and De Andrade2018; Kiefte and Armson, Reference Kiefte and Armson2008; Ritto et al., Reference Ritto, Costa, Juste and de Andrade2016). The facilitating effect of unison production for PWAs has been documented across a few studies (Fridriksson et al., Reference Fridriksson, Hubbard and Hudspeth2012; Kershenbaum et al., Reference Kershenbaum, Nicholas, Hunsaker and Zipse2019, Reference Kershenbaum, Galassi, Shattuck-Hufnagel, Bachan and Zipse2024; Racette, Reference Racette2006). In the aphasia research literature more generally, unison production has been implemented in various ways. In some paradigms, PWAs listen to an utterance and then repeat it in unison (such that the target utterance is known in advance, e.g., Kershenbaum et al., Reference Kershenbaum, Nicholas, Hunsaker and Zipse2019), while in others, the PWAs speak along with utterances that are at least initially unfamiliar (such that they are quickly repeating what they hear, i.e., shadowing; Fridriksson et al., Reference Fridriksson, Hubbard and Hudspeth2012). Another important variable is whether the model utterance is audio-only or includes a view of the speaker’s mouth, as the latter can be facilitating (Fridriksson et al., Reference Fridriksson, Hubbard and Hudspeth2012). MIT relies on repetition so that the target utterance is known, and the clinician typically sits opposite the PWA so their face is visible during articulation.

Explanations for why speaking in unison is helpful for PWAs have included perception–action interactions, possibly involving mirror neurons, and synchrony with a model (Racette, Reference Racette2006; see Chapter 6). Cummins has examined synchronous speech in neurotypical adults without aphasia rather extensively, noting how precisely speakers can align their speech with one another (Reference Cummins2003). He has highlighted entrainment of oscillatory neural activity as a potential mechanism for speech entrainment, though allowed that models need to better account for the complex time structure of typical speech (Cummins, Reference Cummins2009, Reference Cummins2012). More recently, the ability of PWAs to synchronize their speech with a spoken model has been examined under different prosodic timing conditions: typical conversational timing, and beat-based metrical timing, where stressed syllables occur at regular intervals. Both PWAs and control speakers align their speech more precisely with the model in the metrical condition, and also produce a higher percentage of accurate syllables in this condition (Kershenbaum et al., Reference Kershenbaum, Galassi, Shattuck-Hufnagel, Bachan and Zipse2024). The metrical condition, similar to MIT, uses a predictable timing structure that does not rely as heavily on linguistic knowledge (e.g., syntax). This simple, predictable timing structure may allow PWAs to achieve better sensorimotor synchrony with the spoken model and more fully benefit from unison production. More work remains to be done to understand entrainment as a potential facilitating mechanism for PWAs.

43.3.6 Treatment Intensity

A key feature of MIT, as originally conceived, was its intensity: Sparks (Reference Sparks and Chapey2008) stipulated that treatment should take place twice daily. Numerous treatment studies have used high-intensity treatment, for example 1.5 hours/day, five days/week, for approximately 15 weeks (Schlaug et al., Reference Schlaug, Marchina and Norton2008); 30 minutes/day, five days/week, for eight weeks (Zhang et al., Reference Zhang, Yu and Teng2021); and five hours/week for six weeks (Van der Meulen et al., Reference Van der Meulen, Van De Sandt-Koenderman, Heijenbrok, Visch-Brink and Ribbers2014, Reference Van der Meulen, Van De Sandt-Koenderman, Heijenbrok, Visch-Brink and Ribbers2016). Three one-hour sessions/week is a fairly common intensity in MIT treatment studies (Curtis et al., Reference Curtis, Nicholas, Pittmann and Zipse2020; Hough, Reference Hough2010; Zumbansen et al., Reference Zumbansen, Peretz and Hébert2014b). Even this is considerably greater than what is typically used in aphasia treatment in clinical practice, at least in the US, and raises the question of whether promising evidence for MIT is driven in part by the high intensities used in treatment studies (Cavanaugh et al., Reference Cavanaugh, Kravetz and Jarold2021). In fact, across two RCTs, one in the subacute and one in the chronic phase of recovery, intensity was predictive of treatment response to MIT even when age, aphasia severity, and a variety of baseline language measures were not, although time post-onset was also predictive in the subacute phase (Van der Meulen et al., Reference Van der Meulen, Van De Sandt-Koenderman, Heijenbrok, Visch-Brink and Ribbers2014, Reference Van der Meulen, Van De Sandt-Koenderman, Heijenbrok, Visch-Brink and Ribbers2016; although see also Zumbansen and Tremblay, Reference Zumbansen and Tremblay2019). The importance of treatment intensity is one of the principles of neuroplasticity in rehabilitation, as described by Kleim and Jones (Reference Kleim and Jones2008): Sufficient intensity is required to induce neuroplasticity, and adequate treatment duration is critical for behavioral change (“use it and improve it”).

In this regard, the highly facilitating nature of MIT is critical: It makes intensive practice of spoken language both possible and palatable for many people with severe aphasia. Clinical researchers have noted that patients often find MIT motivating and even enjoyable (Merrett et al., Reference Merrett, Peretz and Wilson2014; Sparks et al., Reference Sparks, Helm and Albert1974; Van der Meulen et al., Reference Van der Meulen, Van De Sandt-Koenderman and Ribbers2012). For PWAs who struggle to say single words, the feeling of fluently intoning phrases is likely rewarding. Van der Meulen et al. (Reference Van der Meulen, Van De Sandt-Koenderman, Heijenbrok, Visch-Brink and Ribbers2016) noted that although aphasia treatment studies with higher intensity generally have greater attrition, their own RCT did not have any individuals drop out (Brady et al., Reference Brady, Kelly, Godwin, Enderby and Brady2016). In addition, the structure and repetition of MIT may make it suitable for home practice, a potentially important way to increase intensity in the face of limited provider availability and high treatment costs.

43.3.7 Gradual Progression

Another key feature of MIT is gradual progression through steps of increasing difficulty. Depending on the protocol used, MIT may include three or four levels of difficulty with progressively longer and more challenging utterances. Within each of the levels, the PWA proceeds through several steps for each utterance, with decreasing clinician support (Helm-Estabrooks and Albert, Reference Helm-Estabrooks and Albert2004; Sparks, Reference Sparks and Chapey2008). For example, in Level I (or II, depending on the protocol), there are five steps: (1) the clinician hums the utterance and intones it twice while tapping the PWA’s hand; (2) the clinician and PWA intone the utterance in unison while tapping; (3) the clinician and PWA intone in unison while tapping, but the clinician “fades out” partway through the utterance; (4) the clinician intones the utterance and the PWA repeats it immediately, with tapping for both productions; (5) the clinician intones a question and the PWA responds with the target utterance, with hand tapping. Some studies have used simplified protocols with just one level (e.g., Curtis et al., Reference Curtis, Nicholas, Pittmann and Zipse2020). However, gradual progression through steps with decreasing clinician support is central to MIT.

One major source of variation in treatment response is likely to be exactly how clinician support is implemented, what types of cues are used to facilitate utterance production, and when and how they are discontinued across the steps. For example, one MIT protocol says that in the unison-fade step (step 3 above), the clinician should not lip-synch the utterance after fading out (Helm-Estabrooks and Albert, Reference Helm-Estabrooks and Albert2004). This visual input can be very facilitating, so variation in how and whether it is used is likely quite important (Fridriksson et al., Reference Fridriksson, Hubbard and Hudspeth2012). This same protocol specifies to discontinue utterances not produced after four attempts. In practice, clinicians often use facilitation techniques such as placement cues, other articulatory-kinematic approaches, or written letters or words to help the PWA segment the target if they initially struggle with an utterance (e.g., Dunham and Newhoff, Reference Dunham and Newhoff1979). Such approaches may particularly address AOS.

Gradual progression through steps of increasing difficulty may work through errorless learning, a cognitive rehabilitation approach widely used to support learning in people with amnesia, that has also been used to treat PWAs (Middleton and Schwartz, Reference Middleton and Schwartz2012). In this approach, successful retrieval of a target behavior is practiced repeatedly to promote implicit, Hebbian learning of a desired response. In addition, the progression of MIT provides a motivating context for many repetitions of each utterance. The importance of repetition is a principle of neuroplasticity in rehabilitation (Kleim and Jones, Reference Kleim and Jones2008).

43.3.8 Utterances Targeted in Treatment

43.4.1 MIT as a Framework: What Works for Whom?

What works best on average is not necessarily what works best for a particular individual, especially in a heterogeneous population such as PWAs (even within the subpopulation of individuals who meet the criteria for MIT; see Section 43.2.2). In providing the first detailed protocol for MIT, the originators noted this came with the risk that it would be followed too rigidly, and they encouraged clinicians to modify the protocol to meet each individual’s needs, stating, “The clinical awareness and skill of the person providing clinical service should influence the form of that service” (Sparks and Holland, Reference Sparks and Holland1976, p. 288). Along these lines, Merrett et al. (Reference Merrett, Tailby, Jackson and Wilson2019) note the tension between standardization and customization of a treatment approach, and advocate for “standardized customization” (p. 431). MIT will likely be most powerful if used as a framework, with specific ingredients selected and adjusted for each individual.

Characterizing treatments is particularly complex in rehabilitation more generally. Interventions are behavioral, relying heavily on the interaction between patients and providers, and are typically focused on increasing function and life participation, which results in widely varied goals based on patients’ needs. This creates challenges for clinical effectiveness research, practice, and training (Hart et al., Reference Hart, Dijkers and Whyte2019). To define rehabilitation interventions in a way that allows for a balance of customization (tailoring the treatment to the patient’s needs) and standardization (following a fixed, replicable protocol), a taxonomy is needed (Hart et al., Reference Hart, Dijkers and Whyte2019). While a variety of different frameworks have been proposed, the rehabilitation treatment specification system (RTSS) offers the advantage of defining interventions in the context of treatment theory: Which aspect of function is the treatment targeting, which mechanism(s) of action can effect this change, and which treatment ingredients will be used (Van Stan et al., Reference Van Stan, Whyte and Duffy2021; Whyte et al., Reference Whyte, Dijkers and Hart2014)? Applying the RTSS to MIT is a promising way to organize the evidence pertaining to each ingredient, and highlight gaps for future research to address.

43.4.2 Research Design Considerations

Reviews of the evidence for MIT’s effectiveness often suggest that more and larger-scale RCTs are needed. Before undertaking such trials, critical individual factors that affect treatment responsiveness need to be better understood. Researchers can take advantage of the heterogeneity inherent to the population of PWAs by using single-subject and within-group research designs to understand varied treatment responses, relate these to individual factors, and explore mechanisms of action (Merrett et al., Reference Merrett, Tailby, Jackson and Wilson2019; Thompson, Reference Thompson2006). MIT treatment studies often describe the treatment protocol in a very cursory way, particularly in studies that include an imaging component. This makes it difficult to understand how the treatment worked. Treatment studies must carefully specify which ingredients are used and how they are implemented so that work can be replicated and variation across studies can be accounted for in later meta-analyses. Meta-analyses using individual participant data (IPD) are a useful means for combining data across studies while offering the flexibility to account for variation in participants and treatment factors (Popescu et al., Reference Popescu, Stahl and Wiernik2022; Zumbansen and Tremblay, Reference Zumbansen and Tremblay2019).

44.2.1 Phonetic Mechanisms

The core of the phonetic changes that occur across different temporal and social/regional scales are the individual episodes of auditory–motor interaction of speakers during conversations. Exemplar theory assumes that speakers taking part in conversations are exposed to the phonetic variation that exists in a community and thereby continuously update the knowledge they have of their language (Blevins, Reference Blevins2004; Harrington et al., Reference Harrington, Kleber, Reubold, Katz and Assmann2019b). Like in other domains of motor action (e.g., Cracco et al., Reference Cracco, Bardi and Desmet2018), there is a tendency also in speech to covertly imitate others’ actions, leading to alignment of fine phonetic details of interlocutors’ speech in conversational interaction (Garrod and Pickering, Reference Garrod and Pickering2009). Implicit imitation may include virtually all phonetic aspects, for example, vocal pitch (Bradshaw and McGettigan, Reference Bradshaw and McGettigan2021), vowel quality (Delvaux and Soquet, Reference Delvaux and Soquet2007), plosive consonant articulation (Shockley et al., Reference Shockley, Sabadini and Fowler2004), speech rate (Schultz et al., Reference Schultz, O’Brien and Phillips2016), or prosody (Weise et al., Reference Weise, Levitan, Hirschberg and Levitan2019), especially speech rhythm (Polyanskaya et al., Reference Polyanskaya, Samuel and Ordin2019). Changes are usually subtle and only measurable using acoustic parameters or sensitive perceptual paradigms.

The degree of phonetic adaptation depends on the type of interaction. Experimental paradigms of joint speech have often been based on semi-interactive laboratory tasks, such as repetition or close shadowing of prerecorded speech, and it is still unclear if phonetic convergence in naturalistic interactions and in such experimental settings rely on the same mechanisms (Pardo et al., Reference Pardo, Urmanche and Wilman2018). Other factors reported to play a role are social proximity and preference (e.g., Babel, Reference Babel2012; see below). Most importantly, the time and frequency of exposure to a specific phonetic variant and the density of interactions in a community have an impact on how salient and persistent a phonetic change will be (Blevins, Reference Blevins2004). Agent-based computational modeling has been used to simulate the propagation of phonetic change and predict the macroscopic changes that emerge from subtle phonetic variation within an ensemble of interacting agents when phonetic or social variables are controlled experimentally (for a recent overview, see Harrington et al., Reference Harrington, Kleber, Reubold, Katz and Assmann2019b).

44.2.2 Social versus Cognitive Models

As outlined above, there is evidence that the degree to which interlocutors involved in a conversation converge or diverge in their pronunciation may depend on their social closeness or mutual liking (e.g., Pardo et al., Reference Pardo, Gibbons, Suppes and Krauss2012; for an overview and discussion, see Ruch et al., Reference Ruch, Zürcher and Burkart2018). Phonetic aspects of pronunciation interact with social patterns and often have a social meaning, which has led several authors to identify social pressure as a driving force of sound change (Eckert, Reference Eckert2012; Labov, Reference Labov1963; Polyanskaya et al., Reference Polyanskaya, Samuel and Ordin2019). Based on such evidence, communication accommodation theory (CAT) (Giles and Ogay, Reference Giles, Ogay, Whaley and Samter2007; Giles et al., Reference Giles, Taylor and Bourhis1973) considers linguistic convergence or divergence as specific communication strategies that speakers use to signal their attitudes towards other individuals and create, maintain, or decrease social distance in interaction.

In a contrasting approach, Pickering and Garrod (Reference Pickering and Garrod2004, Reference Pickering and Garrod2013) postulated that speaker accommodation in conversation is driven by implicit cognitive processes rather than explicit social strategies, with prediction and forward modeling as the core mechanisms conveying adaptation. In their interactive alignment model of speaker–listener interactions, conversation is understood as a production-comprehension process in which speakers compute forward models to predict and possibly adjust their planned utterances to the perceptual needs of their interlocutors, and listeners in turn covertly imitate the speakers’ utterances and derive their own forward models to predict what their counterparts will shortly produce. Thus, in conversation we are implicitly modeling our interlocutors who, in turn, are modeling us in a reciprocal exchange of sensory signals (Friston and Frith, Reference Friston and Frith2015). This interaction is considered to reduce the computational processing load and facilitate the mutual understanding of speakers/listeners and, at the same time, lead to a convergence at semantic, syntactic, lexical, and phonetic-phonological levels and a temporal alignment of their interaction. In phonetic terms, the perceptual forward model that a listener creates of an interlocutor’s utterance is shaped by phonetic details of that utterance, which can then merge with the motor forward model listeners compute for their own response and so lead to phonetic convergence (for discussions, see Bradshaw and McGettigan, Reference Bradshaw and McGettigan2021; Ruch et al., Reference Ruch, Zürcher and Burkart2018).

44.2.3 The Role of Speech Rhythm

Rhythmic stimuli are considered to offer an invitation – or even constitute an affordance – for perceivers to coordinate their behavior with the stimulus, as in dancing or clapping one’s hands to a piece of music (“rhythmic entrainment”; Cummins, Reference Cummins2009). The regular recurrence of a beat facilitates sensorimotor predictions about the occurrence of subsequent events and thereby allows individuals to synchronize their actions with the stimulus. Neural entrainment theories assume that alignment to a rhythmic stimulus is mediated by endogenous oscillatory brain activity at a frequency corresponding to the frequency of the stimulus (Lakatos et al., Reference Lakatos, Gross and Thut2019). Speech, with its recurring sonority peaks of syllabic structure and its alternations between stressed and unstressed syllables, has a quasi-rhythmic envelope that, in line with neural entrainment assumptions, evokes an oscillatory auditory cortical response in the delta (1–3 Hz) and theta range (4–8 Hz) coupled with the frequency modulation of the speech signal (Lakatos et al., Reference Lakatos, Gross and Thut2019; see Chapter 3; for a critical view, see Oganian et al., Reference Oganian, Kojima and Breska2023). Thus, in conversational interactions, endogenous oscillators in the brains of the speaker and the listener are assumed to become entrained to the speaker’s prosodic (delta) and syllabic (theta) rhythm. This rhythmic speech–brain entrainment promotes speech understanding (Riecke et al., Reference Riecke, Formisano, Sorger, Başkent and Gaudrain2018) and enables perceivers to predict upcoming linguistic content (Kösem et al., Reference Kösem, Bosker and Takashima2018). Likewise, it allows listeners to predict the end of the speaker’s turn and prepare for their own seamless turn-taking (Wilson and Wilson, Reference Wilson and Wilson2005). Domain-general mechanisms of reaction speed enhancement through expectancy-driven delta oscillations, as described by Stefanics et al. (Reference Stefanics, Hangya and Hernádi2010), may contribute to the smoothness of turn transitions and regulate the perception–action cycle of conversational interactions (see Chapter 6). As hypothesized in Pickering and Garrod’s interactive alignment model, phonetic adaptation during conversation emerges through mutual covert imitation and internal forward modeling of each other’s speech, interwoven with the speaker’s forward modeling of their own speech (Pickering and Garrod, Reference Pickering and Garrod2013). On this background, rhythmic speech–brain entrainment can be understood as a neural mechanism that establishes temporal synchrony between the speakers’ and listeners’ forward models, thereby strengthening the coupling of production and comprehension.

44.4.1 Post-Stroke Aphasia and AOS

Infarctions of the left middle cerebral artery, which most often cause language disorder, usually involve damage to components of the dorsal and/or ventral auditory stream, that is, the neural connectivity considered to implement the “perception–action pathway” that is at the center of interactive alignment theories (Pickering and Garrod, Reference Pickering and Garrod2007; Scott et al., Reference Scott, McGettigan and Eisner2009). Research has focused on the impact of lesions to the anterior versus posterior sections of this pathway on phonetic adaptation. In some studies, the anterior–posterior distinction has been implicitly equated with a contrast between “non-fluent” aphasia syndromes, that is, Broca’s aphasia, on the one hand, and “fluent aphasia” with phonological impairment, on the other, although syndrome classifications and the fluent versus non-fluent dichotomy provide only a vague account of impaired language processing components and affected cortical areas (Caramazza and Badecker, Reference Caramazza and Badecker1989).

In studies by Kappes et al., phonetic adaptation was examined in two contrastive cases of aphasia after anterior versus posterior lesions (Kappes et al., Reference Kappes, Baumgaertner, Peschke and Ziegler2009) and in a case series of individuals with left- versus right-hemisphere strokes (Kappes et al., Reference Kappes, Baumgaertner, Peschke, Goldenberg and Ziegler2010). In a word repetition task, participants overtly repeated pseudo-noun phrases, that is, disyllabic pseudowords preceded by the plural article “die” (e.g., /di:.’dai.gəl/), with stress on the first syllable of the pseudoword and a final schwa-syllable. Two phonetic parameters were varied along a continuum, that is, (i) the degree of pitch elevation on the stress-carrying syllable and (ii) the degree of hyper- or hypo-articulation of the schwa-syllable. The two paradigms represent naturally occurring variations of word stress and phonetic reduction in German. The two patients examined in Kappes et al. (Reference Kappes, Baumgaertner, Peschke and Ziegler2009) showed a clear dissociation in their propensity to adapt in word repetition: A patient with lesions in the anterior opercular, precentral, and anterior insular cortex clearly imitated the gradual phonetic variation in both parameters, whereas a second patient with lesions in inferior-parietal cortex and superior and middle temporal gyrus did not align with the model in either of the two parameters. In the case series of Kappes et al. (Reference Kappes, Baumgaertner, Peschke, Goldenberg and Ziegler2010), individuals with aphasia after left-hemisphere lesions showed a significantly lower degree of imitation in both phonetic paradigms as compared to neurotypical controls and individuals with right-hemisphere lesions. A voxel-wise analysis of the influence of lesion location on imitation in the left-hemisphere group revealed two closely neighboring regions centered in Heschl’s gyrus that were negatively associated with the degree of covert imitation of both parameters. On the contrary, lesions to anterior language areas did not influence imitation behavior. Taken together, these results suggest a role of auditory mechanisms in phonetic adaptation and are not supportive of the motor hypothesis of conversational alignment put forward by Castellucci et al. (Reference Castellucci, Kovach, Howard, Greenlee and Long2022) and Scott et al. (Reference Scott, McGettigan and Eisner2009). Note, however, that the motor hypothesis originally relates to interactions involving larger stretches of connected speech and prediction mechanisms based on the rate and rhythm of incoming speech signals (Park et al., Reference Park, Ince, Schyns, Thut and Gross2015), whereas the adaptation paradigms reported above used isolated single-word utterances that did not provide any space for rhythmic entrainment.

Temporal alignment in connected speech was addressed more specifically in a sequential synchronization experiment by Aichert et al. (Reference Aichert, Lehner and Falk2021). In this study, 12 patients with AOS and 12 patients with phonological impairment participated in a spoken sentence-completion task. AOS is a speech motor-planning disorder that is usually associated with aphasia due to lesions of anterior language areas, whereas lexical or post-lexical phonological impairment is associated with lesions located more posteriorly along the dorsal stream (Schwartz et al., Reference Schwartz, Faseyitan, Kim and Coslett2012). Participants in Aichert et al. (Reference Aichert, Lehner and Falk2021) were required to complete sentence fragments (the “primes”) “as smoothly as possible” by pre-specified, semantically compatible target words. Each prime sentence consisted of four disyllabic words (e.g., “le.na|pflanz.te|da.mals|die.se _”; English (literally) “Lena planted then this _”; periods indicate syllable boundaries, vertical strokes word boundaries; stressed syllables in bold) and had to be completed by a given disyllabic noun (e.g., “tul.pe”; English “tulip”). Half of the primes had a regular trochaic rhythm (as in the cited example) and half were metrically irregular, with alternations of trochaic and iambic words and a stress clash within the sentence. The target words were trochees (e.g., tul.pe) or iambs (e.g., te.nor) and were orthogonally arranged with the prime sentences into rhythmically compatible and incompatible prime-target pairs (for trochaic versus iambic meters, see Chapter 32). Response latency, that is, the time interval between the onset of the model speaker’s last word and the onset of the participant’s response, was used as a measure of entrainment. In neurotypical participants, metrically regular response words complementing metrically regular prime sentences were initiated almost precisely “in time with the beat”; that is, response latencies matched the mean duration of the metrical feet of the corresponding prime sentence. This suggests that in the rhythmically regular condition, participants entrained perfectly with the metrical pace of the model speaker’s utterances. After prime sentences with irregular rhythms, trochaic target words were initiated with somewhat shorter delays, suggesting that while hearing a rhythmically irregular prime sentence, the participants may have been unable to create a rhythmical framework for their response and therefore tried to produce the target word as quickly as possible (Aichert et al., Reference Aichert, Lehner and Falk2021). When the target words had an iambic meter, response latencies were consistently increased by ca. 30 ms in both prime conditions, consistent with the assumption that the much less frequent iambic pattern of lexical stress comes with a production disadvantage relative to the considerably more frequent trochaic pattern (Aichert et al., Reference Aichert, Späth and Ziegler2016).

Compared with this response pattern, patients with aphasia, especially those with a concomitant motor speech impairment, had substantially longer response latencies, which may have been due to an unspecific effect of the brain lesion or the speech-language disorder. However, more importantly, in both aphasia groups, response latencies were not modulated to any significant extent by the rhythmic pattern of the prime sentences; that is, the patients with aphasia did not entrain with the regular speech rhythm of the model speaker in terms of the timing of their responses. In the speech-apraxic patients, this outcome appears consistent with the motor hypothesis of interactive alignment advocated by Castellucci et al. (Reference Castellucci, Kovach, Howard, Greenlee and Long2022) or Scott et al. (Reference Scott, McGettigan and Eisner2009), whereas in the patients with intact motor speech, disruption of auditory aspects of the perception–action mechanism involved in interactive alignment, as suggested by Kappes et al. (Reference Kappes, Baumgaertner, Peschke, Goldenberg and Ziegler2010), may be more plausible. However, speech error analyses of the target-word responses revealed that individuals with aphasia with or without AOS made fewer errors in responses to regular than to irregular primes (Aichert et al., Reference Aichert, Lehner, Falk, Späth and Ziegler2019). This seems to suggest that they implicitly did profit from the rhythmical regularity of another speaker’s utterances, if not in terms of temporal alignment then at least in terms of the accuracy of their responses.

Speech entrainment methods have long been recognized as an efficient intervention strategy in individuals with AOS, for example through synchronous production of training items, where the clinician instructs the patient “to attend carefully to the auditory and especially to the visual cues of correct production as they say the utterance together” (e.g., Rosenbek et al., Reference Rosenbek, Lemme, Ahern, Harris and Wertz1973, p. 464). Since Rosenbek’s joint-speech intervention in speech apraxia did not necessarily involve continuous speech at the level of phrases or even texts, its effect was not easily explainable by rhythmic entrainment mechanisms. In contrast, the synchronous or choral speaking methods described and discussed by Cummins (Reference Cummins2003, Reference Cummins2009) and applied, for example, in stuttering therapy, involve synchronous production of longer stretches of continuous speech and are explicitly considered to rely on rhythmic entrainment (for a discussion, see Bradshaw and McGettigan, Reference Bradshaw and McGettigan2021; see also Chapters 45 and 46). Fridriksson et al. (Reference Fridriksson, Hubbard and Hudspeth2012) implemented a computerized version of audiovisual entrainment in which patients mimic, in real time, a videotaped speaker producing short scripts, and found a significant fluency-enhancing effect in patients with Broca’s aphasia. Johnson et al. (Reference Johnson, Yourganov and Basilakos2022) hypothesized that the improvements achieved by this method are ascribable to a synchronization of activations in anterior and posterior language areas, potentially through rhythmic entrainment mechanisms as described by Assaneo and Poeppel (Reference Assaneo and Poeppel2018) and others (see Section 44.3).

44.4.2 Parkinson’s Disease (PD)

It has repeatedly been shown that auditory rhythmic stimulation through the beat of music or of a metronome enhances movement control in individuals with PD (see Chapter 45). Most of the studies of rhythmic entrainment by such nonlinguistic auditory cues have addressed gait problems and reported greater stride length and higher walking speed (Nombela et al., Reference Nombela, Hughes, Owen and Grahn2013), while only few studies reported on therapeutic applications addressing speech impairments (e.g., Thaut et al., Reference Thaut, Mcintosh, McIntosh and Hoemberg2001). The beneficial effects of auditory–motor synchronization were explained within a theoretical framework of self-paced movements in which akinesia in PD is ascribed to a breakdown of a basal ganglia-thalamocortical circuit supporting attention-dependent motor timing and initiation mechanisms (Schwartze et al., Reference Schwartze, Keller, Patel and Kotz2011). In rhythmic auditory–motor synchronization, this dysfunctional mechanism is thought to be compensated by a cerebellar-thalamocortical circuit that supports the matching of movements to an external rhythmic template (for an outline, see Dalla Bella et al., Reference Dalla Bella, Benoit, Farrugia, Schwartze and Kotz2015; see also Chapter 45).

Compared with a metronome sound or a piece of music with a prominent beat, the speech signal of an interacting interlocutor presumably has much less rhythmic salience. One may therefore ask if individuals with PD entrain with the rhythm of others’ speech to a similar extent as they entrain with music or a metronome. This question was addressed in the sentence-completion experiment already outlined in Section 44.4.1 (Aichert et al., Reference Aichert, Lehner and Falk2021), which, along with the two aphasia groups described above, also included a group of individuals with PD. In this study it turned out that the speakers with PD showed the same response pattern as the neurotypical participants; that is, they completed the model speaker’s metrically regular prime sentences with a delay that was adjusted to the perceived speech rhythm. Thus, speakers with PD, like healthy speakers, appeared to anticipate the point in time when their response fitted into the rhythmic pattern of the model speaker’s utterance (Aichert et al., Reference Aichert, Lehner and Falk2021).

In a later study, Späth et al. (Reference Späth, Aichert and Timmann2022) examined if basal ganglia dysfunction in PD not only preserves the ability to temporally align with others’ speech but also the propensity to covertly imitate another speaker’s phonetic idiosyncrasies. Two semi-interactive paradigms were used in this study to elicit sentence utterances, that is, a sentence repetition paradigm where participants were instructed to simply repeat a model speaker’s prerecorded sentence, and a pseudo-dialogue paradigm where participants answered to the model speaker’s sentence by producing a scripted response sentence, such as in a short dyadic exchange. In a control condition, participants read the test sentences aloud. Speech rate in the model sentences was varied experimentally between 2.9 and 4.0 syllables per second. The study included 15 individuals with PD, along with 12 individuals with spinocerebellar ataxia, type 6 (SCA6; see Section 44.4.3), and 27 neurotypical controls. The research question was whether patients with PD would covertly imitate the individual rate and rhythm of the model sentences. Regarding speech rate, a linear regression model revealed that the PD group followed the item-to-item changes in the model speaker’s speech rate in the sentence repetition paradigm by a proportion of more than 30%. In the pseudo-dialogues, the degree of adaptation was smaller (ca. 20%), but still significant. There was no difference between the healthy and PD participants. On an individual basis, significant rate adaptation was found in 80% of both neurotypical and PD individuals.

To assess rhythm adaptation, the individual rhythm of each spoken sentence was represented by a vector of nine inter-beat intervals, that is, the intervals between the p-centers of the 10 successive syllables of a sentence (for p-centers, see Chapter 11). The closeness of a participant’s sentence rhythm to the associated model sentence was determined as the Euclidean distance between the two corresponding vectors of inter-beat intervals. Adaptation occurred when the Euclidean distance between the participant’s and the model speaker’s sentence became smaller in the semi-interactive as compared to the noninteractive (i.e., reading) condition. Using this measure, the PD group demonstrated significant rhythm adaptation in both the repetition and the pseudo-dialogue paradigms. Rhythm adaptation was even stronger in the PD than in the control group, with 80% of the individuals with PD versus 52% of the neurotypical participants showing significant rhythm adaptation across all sentences. This finding fits with observations of a high responsiveness of PD patients to external rhythmical stimulation and the hypothesis that they resort to cerebellar mechanisms of matching an external rhythm (Dalla Bella et al., Reference Dalla Bella, Benoit, Farrugia, Schwartze and Kotz2015). Hence, the basal ganglia dysfunction underlying PD obviously did not prevent participants from covertly adapting to a model speaker’s rate and rhythm, at least in experimental paradigms that promote attention to external stimuli, such as sentence repetition (Späth et al., Reference Späth, Aichert and Timmann2022; for a similar result, see Späth et al., Reference Späth, Aichert and Ceballos-Baumann2016).

44.4.3 Cerebellar Degeneration

Compared with basal ganglia disorders, only little is known about the role of the cerebellum in speech entrainment. Therefore, clinical studies of phonetic alignment involving cerebellar pathologies are particularly relevant. As far as temporal alignment and rhythmic entrainment in interactive language use is concerned, the critical involvement of cerebellar-thalamocortical circuits in motor and perceptual timing in the sub-second range (Buhusi and Meck, Reference Buhusi and Meck2005; Konoike and Nakamura, Reference Konoike and Nakamura2020) and in rhythmic auditory–motor synchronization (Thaut et al., Reference Thaut, Stephan and Wunderlich2009) suggest that cerebellar dysfunction would presumably interfere with smooth turn-taking in conversations or with synchronized speaking. By contrast to this assumption, however, Breska and Ivry (Reference Breska and Ivry2018) put the cerebellum’s contribution to perceptual timing into perspective by showing that temporal prediction based on rhythmic (visual) cues was preserved in patients with spinocerebellar ataxia. Similarly, Breska and Ivry (Reference Breska and Ivry2016) suggested that motor timing based on rhythms emerging implicitly from motor control parameters, such as in the repetitive drawing of circles, is unimpaired in patients with cerebellar degeneration. However, it remains open whether the conclusions drawn from such paradigms can be extrapolated to phonetic alignment in interactive speech.

A different line of research ties in with the well-established role of cortico-cerebellar circuits in the representation of forward models in motor control, that is, in the prediction of the sensory consequences of one’s own planned movements (Wolpert et al., Reference Wolpert, Miall and Kawato1998; see Chapter 6). The “predictive cerebellum” concept has in recent years been translated to the domain of action observation, suggesting that the cerebellum is not only engaged in internal sensorimotor forward modeling but also in the prediction of the consequences of motor actions observed in others (Abdelgabar et al., Reference Abdelgabar, Suttrup and Broersen2019). Even more generally, cerebro-cerebellar forward models have been considered as a mechanism to understand and predict the outcome of others’ behaviors, not only in sensorimotor terms but also in social cognition and affective processing (Sokolov et al., Reference Sokolov, Miall and Ivry2017; Van Overwalle et al., Reference Van Overwalle, Manto and Cattaneo2020). Transferred to language comprehension in conversational interactions, the posterior cerebellum was shown to be engaged in using earlier context in a perceived sentence to predict what an interlocutor is going to say in the further course of the sentence (e.g., Moberget et al., Reference Moberget, Gullesen, Andersson, Ivry and Endestad2014). This places the predictive functions of the cerebellum at the center of “active inference” (Friston and Frith, Reference Friston and Frith2015), in cognitive sequencing (Morgan et al., Reference Morgan, Slapik and Iannuzzelli2021) or in interactive alignment models (Pickering and Garrod, Reference Pickering and Garrod2013), and leads to the prediction that cerebellar pathology would interrupt the propensity of individuals to align with and adapt to others in interactive speech.

This hypothesis was tested in 12 individuals with SCA6 who were included in the rate- and rhythm adaptation experiment described in Section 44.4.2 (Späth et al., Reference Späth, Aichert and Timmann2022). Recall that in this experiment, neurotypical individuals and individuals with PD showed similarly clear tendencies to adapt to a model speaker’s speech rate and rhythm, both in sentence repetition and in dialogue-like sentence dyads. In remarkable contrast to this, the spinocerebellar group showed hardly any adaptation, neither of speech rate nor of rhythm, and none of the SCA6 individuals adapted to both rate and rhythm to a statistically significant extent. Interestingly, the proportion to which SCA6 patients aligned with the model speaker’s rate and rhythm did not depend on general motor or speech motor abilities assessed by ataxia-rating scales and a dysarthria test, and also not on auditory perceptual abilities assessed by an auditory rate discrimination test. Hence, the reduced propensity of individuals with cerebellar degenerative disorders neither resulted from purely auditory nor from purely motor dysfunctions as far as they could be assessed experimentally. Späth et al. (Reference Späth, Aichert and Timmann2022) proposed an explanation that links up with the generalized forward-modeling account of cerebellar function, suggesting that cerebellar degeneration impairs listeners’ forward modeling and prediction of their interlocutors’ speech in conversational interactions and thereby undermines the cognitive processes supporting interactive alignment and phonetic convergence (Späth et al., Reference Späth, Aichert and Timmann2022).

45.4.1 Stuttering in PD

Although dysarthria is the most prominent speech motor disorder in relation with PD (e.g., Duffy, Reference Duffy2019), stuttered dysfluencies are also found, in particular in more severe and longer-term cases of PD (Benke et al., Reference Benke, Hohenstein, Poewe and Butterworth2000; Gooch et al., Reference Gooch, Horne and Melzer2023). In the few studies available (see Gooch et al, Reference Gooch, Horne and Melzer2023, for a summary), estimates of new-onset stuttering in PD patients range between 4 and 53%. Individuals who had once remitted from childhood stuttering were also found to present stuttering again at the onset of PD (Shahed and Jankovic, Reference Shahed and Jankovic2001).

45.4.2 “Freezing of Gait” versus “Gluency” in Speech

Alm (Reference Alm2021) recently discussed similarities between gait freezing in PD and stuttering in the “inability to move forward in a movement sequence,” whether gait or speech. Freezing of gait implies a blockade appearing as a failure in gait initiation, or occurring abruptly while patients are walking; in the latter case, a sudden decrease of step length and increase of step frequency and step-to-step variability is observed prior to a complete blockade, which may lead to falling (e.g., Grabli et al., Reference Grabli, Karachi and Welter2012). For stuttering, the term “gluency” has been coined for the subjective experience of being stuck and unable to control the articulators as wished (Van Riper, Reference Van Riper1992).

45.4.3 Effects of Auditory Pacing

In developmental stuttering, we find “fluency-enhancing conditions” that can significantly reduce stuttering, sometimes to no stuttering symptoms at all. There are different forms of these conditions. Some of them temporarily change the way auditory feedback of speech is delivered (e.g., whispering, delaying auditory feedback, auditory masking with noise or music). Others provide auditory rhythmic cues or rhythmic enhancement (i.e., speaking with a metronome, singing, choral speech; Andrews et al., Reference Andrews, Howie, Dozsa and Guitar1982; Ingham et al., Reference Ingham, Bothe and Jang2009). However, the beneficial effect of fluency-inducing conditions wanes after stopping the cue or altered feedback. Interestingly, stuttering in PD has a long tradition of being described as neurogenic stuttering that should not respond to such fluency enhancements (Krishnan and Tiwari, Reference Krishnan and Tiwari2013). However, individuals with PD who stutter were found to respond to choral speech similar to individuals with developmental stuttering (Juste et al., Reference Juste, Sassi, Costa and de Andrade2018). Moreover, articulatory tools used in the therapy of neurodevelopmental stuttering, such as slowing speech rate as well as other articulatory techniques, can also help individuals with PD who stutter with their speech. These results require more studies about the common grounds for speech and other dysfluencies in PD and developmental stuttering.

The effect of auditory pacing in PD is more evident. Known as rhythmic auditory cueing (RAC), presenting a regular auditory stimulus such as a metronome or music with a salient beat improves significantly gait in PD patients (Fleming, Reference Fleming1942; Ghai et al., Reference Ghai, Ghai, Schmitz and Effenberg2018b; Kwakkel et al., Reference Kwakkel, de Goede and van Wegen2007). The intervention involves instructing patients to walk in synchrony with a regular sound or music with a distinct beat, often tailored to their preferred cadence (Benoit et al., Reference Benoit, Dalla Bella and Farrugia2014; Elston et al., Reference Elston, Honan, Powell, Gormley and Stein2010; Enzensberger et al., Reference Enzensberger, Oberländer and Stecker1997; Howe et al., Reference Howe, Lövgreen, Cody, Ashton and Oldham2003; McIntosh et al., Reference McIntosh, Brown, Rice and Thaut1997; Thaut et al., Reference Thaut, McIntosh and Rice1996). In the presence of a rhythmic stimulus, PD patients typically walk faster, increase their step length (McIntosh et al., Reference McIntosh, Brown, Rice and Thaut1997), and reduce the frequency of freezing episodes (Arias and Cudeiro, Reference Arias and Cudeiro2010). As in stuttering, this immediate effect tends to disappear after the end of the stimulation.

45.4.4 Shared Mechanisms

Both stuttering and PD share a common rhythmic component associated with difficulties in generating precise internal timing for motor actions, such as gait coordination and speech articulation. This rhythmic deficit involves inaccurate temporal predictions governing motor commands, resulting in disrupted movement initiation or execution. Interestingly, in both cases, the presence of an external rhythmic stimulus can alleviate these difficulties by compensating for the internal prediction inaccuracies. These observations support the characterization of stuttering and PD as motor-rhythm disorders. Both conditions involve alterations in the neuronal circuitry (subcortical-cortical network) underlying rhythm perception, production, and temporal prediction. Tasks involving beat perception and synchronization recruit similar neuronal circuitries, including the basal ganglia, premotor cortex, and pre-supplementary motor area (Chen et al., Reference Chen, Penhune and Zatorre2008; Coull et al., Reference Coull, Cheng and Meck2011; Dalla Bella et al., Reference Dalla Bella, Benoit and Farrugia2017; Grahn and Brett, Reference Grahn and Brett2007; Grahn and Rowe, Reference Grahn and Rowe2009; Repp, Reference Repp2005; Repp and Su, Reference Repp and Su2013; Schwartze and Kotz, Reference Schwartze and Kotz2013).

Another important aspect of rhythm deficits in both stuttering and PD is the role of automatization. In PD, neurodegeneration leads to de-automatization of movement, resulting, among other symptoms, in poorer dual-task performance. This aspect seems to be less evident in stuttering. However, Alm (Reference Alm2021) recently proposed that stuttering may involve less automatized speech sequences within the basal ganglia motor loop during childhood (see also Chang and Guenther, Reference Chang and Guenther2020). Stuttering symptoms would emerge during attempts to produce these poorly automatized sequences. De-automatization of speech production through the allocation of increased attentional resources, such as imitating others, speaking with an accent, or consciously altering speech rate and articulatory patterns, would then reduce stuttering. The effects of rhythmic fluency-enhancing conditions can be interpreted in a similar way, as strong beat-based rhythms provided by a metronome provide a temporal scaffolding supporting temporal predictions (Large and Jones, Reference Large and Jones1999; Schwartze and Kotz, Reference Schwartze and Kotz2013) capable of freeing up attentional resources.

45.5.1 Training Exploiting Rhythmic Stimulation in PD

Several treatments are available to manage motor symptoms in PD. These include medication (such as levodopa and dopamine agonists) (Connolly and Lang, Reference Connolly and Lang2014), surgical procedures (such as pallidotomy or thalamotomy) (Lozano et al., Reference Lozano, Tam and Lozano2018), deep-brain stimulation (DBS) (Benabid et al., Reference Benabid, Pollak, Louveau, Henry and de Rougemont1987; Kalia et al., Reference Kalia, Sankar and Lozano2013), and noninvasive options such as physical therapy and neuromodulation techniques (transcranial magnetic stimulation or transcranial direct-current stimulation) (Benninger and Hallett, Reference Benninger and Hallett2015). Each treatment aims to compensate for dopamine loss or reduce the dysfunction in brain circuitries related to movement. In addition to pharmacotherapy and various other interventions, non-pharmacological treatments such as RAC are recognized for their beneficial effects in managing PD symptoms. These rhythm-based therapies complement traditional treatments and are increasingly acknowledged for their role in improving motor symptoms in PD patients. More generally, rhythmic stimuli have shown beneficial effects on motor behavior in patients with movement disorders and older adults (Ghai et al., Reference Ghai, Ghai and Effenberg2018a, Reference Ghai, Ghai, Schmitz and Effenberg2018b; Spaulding et al., Reference Spaulding, Barber and Colby2013). Most studies have focused on gait disorders due to their functional relevance, impact on quality of life, and economic burden. In the management of PD, where the effectiveness of dopamine replacement therapy diminishes over time (Grabli et al., Reference Grabli, Karachi and Welter2012; Sethi, Reference Sethi2008), there is a pressing need for innovative non-pharmacological approaches to improve gait. Rhythm-based interventions, such as walking to an auditory beat or participating in dance activities, hold promise in enhancing gait, quality of life, and social engagement among individuals with PD (for a review, see Dalla Bella, Reference Dalla Bella, Cuddy, Belleville and Moussard2020). These interventions leverage rhythmic auditory cues to provide a temporal framework that facilitates movement initiation and coordination (Ghai et al., Reference Ghai, Ghai, Schmitz and Effenberg2018b; Spaulding et al., Reference Spaulding, Barber and Colby2013).

RAC has an immediate beneficial effect on gait in PD (Arias and Cudeiro, Reference Arias and Cudeiro2010; Cochen De Cock et al., Reference Cochen De Cock, Dotov and Ihalainen2018; McIntosh et al., Reference McIntosh, Brown, Rice and Thaut1997). While these benefits tend to dissipate once the stimulation ceases, longer-term effects can be observed through RAC rehabilitation programs, as shown by Lim et al. (Reference Lim, van Wegen and de Goede2005). These programs involve regular RAC-assisted walking sessions, resulting in increased walking speed and reduced freezing phenomena at the end of the rehabilitation, even in the absence of stimulation (Dalla Bella et al., Reference Dalla Bella, Benoit and Farrugia2017; Nieuwboer, Reference Nieuwboer2008; Rochester et al., Reference Rochester, Burn, Woods, Godwin and Nieuwboer2009). Similar motor benefits are observed with home-based RAC rehabilitation using a stimulating device (Nieuwboer et al., Reference Nieuwboer, Kwakkel and Rochester2007). However, the long-term persistence of these effects and their interaction with neurodegenerative decline remain uncertain, with inconclusive evidence to date (Benoit et al., Reference Benoit, Dalla Bella and Farrugia2014; Marchese et al., Reference Marchese, Diverio, Zucchi, Lentino and Abbruzzese2000; Nieuwboer et al., Reference Nieuwboer, De Weerdt and Dom2001).

The exact nature of brain mechanisms underlying these beneficial effects still needs clarification. For example, the bases of RAC in PD is still a subject of ongoing debate (Dalla Bella et al., Reference Dalla Bella, Benoit, Farrugia, Schwartze and Kotz2015, Reference Dalla Bella, Dotov, Bardy and Cochen de Cock2018; Nombela et al., Reference Nombela, Hughes, Owen and Grahn2013; for a review, see Dalla Bella, Reference Dalla Bella, Cuddy, Belleville and Moussard2020). It is still unclear whether beneficial effects are mediated by spared brain mechanisms (cerebello-thalamo-cortical network) acting compensatorily, or by capitalizing on residual capacities of the impaired network in PD (basal ganglia-thalamo-cortical network). However, emerging evidence suggests that these mechanisms may extend beyond gait-specific processes and instead involve a more general-purpose network that supports rhythm perception, production, and temporal prediction (Dalla Bella, Reference Dalla Bella, Cuddy, Belleville and Moussard2020; Large and Jones, Reference Large and Jones1999; Piras and Coull, Reference Piras and Coull2011; Schwartze and Kotz, Reference Schwartze and Kotz2013). This idea is supported by studies indicating improved rhythm perception following RAC training (Benoit et al., Reference Benoit, Dalla Bella and Farrugia2014) and the observation that gait improvement through RAC is associated with individual rhythm perception and production abilities (Cochen De Cock et al., Reference Cochen De Cock, Dotov and Ihalainen2018; Dalla Bella et al., Reference Dalla Bella, Benoit and Farrugia2017, Reference Dalla Bella, Dotov, Bardy and Cochen de Cock2018). Notably, this hypothesis aligns well with the aforementioned general dysrhythmia hypothesis (Cantiniaux et al., Reference Cantiniaux, Vaugoyeau and Robert2010; Puyjarinet et al., Reference Puyjarinet, Bégel and Gény2019; Tolleson et al., Reference Tolleson, Dobolyi and Roman2015) and provides a coherent framework for understanding the broader implications of rhythm interventions.

A hypothesis arising from this theory posits that rhythmic training targeting a specific effector (e.g., hand, finger) may yield positive effects on motor control and rhythmic behavior in other effectors (e.g., oromotor, gait). This transfer effect could be facilitated by the shared mechanisms supporting temporal prediction (Dalla Bella, Reference Dalla Bella2022). To examine this hypothesis, we conducted a recent pilot study (Puyjarinet et al., Reference Puyjarinet, Bégel and Geny2022) involving patients with PD. During the study, participants underwent training using either a rhythmic tapping game (Rhythm Workers; Bégel et al., Reference Bégel, Seilles and Dalla Bella2018; Dauvergne et al., Reference Dauvergne, Bégel and Gény2018) or a nonrhythmic game (Tetris) over the course of one month. Both games were implemented as tablet apps. The rhythmic game required participants to tap along with various rhythmic auditory stimuli, with synchronization accuracy driving progress in the game. Remarkably, the rhythm intervention not only reduced motor variability in the trained motor domain (tapping) but also demonstrated a positive effect on an oromotor task (diadocokinesis task), unlike the control condition that showed no such effect. Moreover, these beneficial outcomes were correlated with improvements in rhythm perception. These promising findings provide evidence of transfer effects driven by rhythmic training in PD, and first causal evidence in support of the hypothesis of a general dysrhythmia in PD. If confirmed in further studies also involving other clinical populations, these findings might have particular significance from a clinical standpoint, whereby the effects of rhythmic training may extend from one effector and motor actions to others. This highlights the potential of rhythmic interventions as a valuable clinical tool for addressing motor impairments in various conditions, including developmental stuttering.

45.5.2 Potential of Rhythmic Training in Developmental Stuttering

Two decades ago, it was proposed that explicitly training the basal ganglia timing network may benefit rhythmic speech production in stuttering (Alm, Reference Alm2004; Fujii and Wan, Reference Fujii and Wan2014). However, to date, no therapeutic approach based on rhythmic pacing techniques has been established, as the effects tend to diminish immediately after the end of rhythmic stimulation. Some devices have been developed to mimic fluency-enhancing conditions, such as choral speech, during naturalistic speech interaction, with mixed results (e.g., Pollard et al., Reference Pollard, Ellis, Finan and Ramig2009). More recently, efforts have been focused on utilizing neuromodulation techniques to establish more efficient patterns of information flow in the brains of adults who stutter. For example, transcranial direct-current stimulation (tDCS), a technique through which brain regions are stimulated with very low electrical current during the execution of a task, was used in combination with rhythmic pacing, such as speaking with a metronome or choral speech, to enhance fluent speech production (Busan et al., Reference Busan, Moret, Masina, Del Ben and Campana2021). Although first results are promising, further research is needed to determine the potential and the exact conditions under which these techniques should be applied.

Further exploration could focus on investigating whether intense or long-term rhythmic training could serve as a naturalistic approach for individuals who stutter to enhance their temporal predictions. An initial step would involve assessing the relationship between musical training and the occurrence, severity, or therapy outcomes of stuttering. If individuals who stutter exhibit significantly lower levels of musical training compared to the general population, or if lower severity or therapy success is associated with musical or rhythmic abilities, this would provide a basis for examining the effects of musical and rhythmic training on stuttering. Currently, only a limited number of studies have investigated the relationship between music and stuttering, primarily regarding immediate fluency-inducing effects, with Falk (Reference Falk and Sammler2025) and Falk et al. (Reference Falk, Schreirer, Russo, Heydon, Fancourt and Cohen2018) providing comprehensive overviews. Secondly, it would be crucial to identify individual differences that distinguish which speakers who stutter would benefit from musical training versus those who would not. Lastly, research should assess whether general musical, nonverbal, or specifically verbal rhythmic training can produce transfer effects on self-paced everyday speech.

46.1.1 The Nature of Rhythm and Its Measurability

‘Rhythm’ in the study of spoken language is used in many different contexts – for example, as an aesthetic property, as a manifestation of a foreign accent, or as a feature of language typology (Hoeqvist, Reference Hoeqvist1983; Barry et al., Reference Barry, Andreeva and Koreman2009; Koreman et al., Reference Koreman, Van Dommelen, Sikveland, Andreeva and Barry2009). What is meant by ‘rhythm’ in these different contexts is primarily an auditory property.Footnote ¹

Phonetic studies of rhythm have recently focused on measurable properties, more specifically, on the durational characteristics of speech (Low, Reference Low1998; Grabe et al., Reference Grabe, Post and Watson1999; Low and Grabe, Reference Low and Grabe1999; Deterding, Reference Deterding2001; Gibbon and Gut, Reference Gibbon and Gut2001; Low et al., Reference Low, Grabe and Nolan2001; Grabe, Reference Grabe, Low, Gussenhoven and Warner2002; Asu and Nolan, Reference Asu and Nolan2005; Russo and Barry, Reference Russo, Barry and Russo2010; see also Chapter 30). This is immediately understandable and plausible in the light of the early, auditory-based statements. Additionally, the concept of an acoustic foundation underpinning auditory language–rhythm discrimination further supports this idea, which reduced rhythmic differences between languages to syllable-timed – that is, with a claim of approximately equal syllabic intervals, or syllabic isochrony – and stress-timed – that is, with a claim of roughly equal foot intervals and reduced syllable durations between the accented syllables (Lloyd, Reference Lloyd1940; Abercrombie, Reference Abercrombie1965).

It is now generally accepted that none of the many attempts to find a physical reflex of isochrony have been successful in the past (Bolinger, Reference Bolinger, Abe and Tanekiyo1965; Wenk and Wioland, Reference Wenk and Wioland1982; Roach, Reference Roach and Crystal1982; Dauer, Reference Dauer1983, Reference Dauer1987; Manrique and Signorini, Reference Manrique and Signorini1983; Eriksson, Reference Eriksson1992; Deterding, Reference Deterding2001; Gibbon and Gut, Reference Gibbon and Gut2001; Wagner, Reference Wagner and Russo2010). Rhythm is no longer considered as a language primitive but rather as an emergent property, the product of phonological structure and phonetic realization. The shift from isochrony to variability led to the breakdown of the initial dichotomy in auditory perception, causing the disconnection of the syllable from its role as a fundamental unit of rhythm. The concept of syllable and foot regularity has been replaced by the degree of syllabic irregularity depending on the range of syllable complexity, with sub-syllabic durational measures as its acoustic basis (for an additional perspective in neuroscience, consider Chapters 3, 5, and 9; also, for the language acquisition view, see Chapters 35 and 36).

Moving somewhat away from what had been defined in the past as syllabic isochrony in all languages, in the new approaches the measurements of durational variation seem to serve more successfully to separate rhythmic types (Low, Reference Low1998; Ramus, Reference Ramus, Nespor and Mehler1999; Ramus et al., Reference Ramus, Nespor and Mehler1999; Grabe and Low, Reference Grabe, Low, Gussenhoven and Warner2002; Barry et al., Reference Barry, Andreeva, Russo, Dimitrova and Kostadinova2003; Russo and Barry, Reference Russo and Barry2004, Reference Russo and Barry2008a, Reference Russo and Barry2008b; Dellwo, Reference Dellwo, Karnowski and Szigeti2006; Mok and Dellwo, Reference Mok and Dellwo2008).

Speech is rhythmically structured in time (Chapter 32; Arvaniti, Reference Arvaniti1994, Reference Arvaniti2009; Cutler, Reference Cutler1994; Barry et al., Reference Barry, Andreeva, Russo, Dimitrova and Kostadinova2003; Russo and Barry, Reference Russo and Barry2008a, Reference Russo and Barry2008b, Reference Russo, Barry and Russo2010; Cummins, Reference Cummins2009; Barry and Andreeva, Reference Barry, Andreeva and Russo2010; Wagner, Reference Wagner and Russo2010). We expect a temporal regularity in the prominent syllables produced in natural, communicatively meaningful speech (see Chapter 35), given the multi-level nature of accentuation. This expectation holds true even if the acoustic basis of rhythm-carrying prominences does not show predictability in each production parameter (i.e., duration, fundamental frequency, intensity, and spectral definition). In a language without lexical stress, such as French (which features a phrase-final demarcative accent), supra-lexical information-based prominence is observed. It’s notable that accentuation effects in French primarily occur at the ends of phrases or stress groups (see Barry and Andreeva, Reference Barry, Andreeva and Russo2010). This prominence disrupts the typical pattern of temporal regularity (isochrony) in syllable sequences. Moreover, in normal speech (with or without a lexical stress system), the rhythm is still carried by the prominences within utterances, but we can rarely find a pattern of regular beats.

The rhythmic differences between language types should be audibly comprehensible, as well as quantitatively demonstrable. And if rhythm is part of a language, such differences should be related to phonology. This connection becomes effective and plausible because of the concepts of mora, syllable, and foot. Thus, the view has grown that the rhythmic character of a language is an emergent property, the product of phonological structure and post-lexical processes in speech production (Bolinger, Reference Bolinger, Abe and Tanekiyo1965; Dauer, Reference Dauer1983, Reference Dauer1987; Low, Reference Low1998; Ramus et al., Reference Ramus, Nespor and Mehler1999; Grabe and Low, Reference Grabe, Low, Gussenhoven and Warner2002; Barry and Russo, Reference Barry, Andreeva, Russo, Dimitrova and Kostadinova2003; Wagner, Reference Wagner and Russo2010).Footnote ² In the task of rhythm measurement, prosodic patterns serve as a guiding factor. This implies that maintaining some degree of isochrony is crucial for the effective functioning of the rhythmic predictor, as rhythmic measurements capture two levels of speech organization. These measurements encompass both segmental and prosodic syllabic structures embedded within phrasal prosody (Couper-Kuhlen, Reference Couper-Kuhlen1993; Auer et al., Reference Auer, Couper-Kuhlen and Müller1999; Barry et al., Reference Barry, Andreeva and Koreman2009).

It is well known that rhythm measures have been conceived to capture the rhythm typology of different languages. They aim to assign the rhythm of an utterance to either the syllable-timed pole, characterized by less variability in vocalic durations, or to the stress-timed pole, which features greater variability in vocalic durations along the rhythmic continuum. Structurally based measures, which basically focus on different degrees of deviation from physical isochrony, appear to have been much more successful in differentiating languages’ rhythmicity (Ramus, Reference Ramus, Nespor and Mehler1999; Ramus et al., Reference Ramus, Nespor and Mehler1999; Low et al., Reference Low, Grabe and Nolan2001; Grabe and Low, Reference Grabe, Low, Gussenhoven and Warner2002; Barry and Russo, Reference Barry, Andreeva, Russo, Dimitrova and Kostadinova2003; Barry et al., Reference Barry, Andreeva, Russo, Dimitrova and Kostadinova2003; Kohler, Reference Kohler2009; Nolan and Asu, Reference Nolan and Asu2009; Chapter 30): Ramus’ delta values (∆C and ∆V) (see Ramus, Reference Ramus, Nespor and Mehler1999; Ramus et al., Reference Ramus, Nespor and Mehler1999), the standard deviation of the vocalic and consonantal intervals within an utterance, with in addition a measure of the vocalic proportion of the utterance (%V); and the pairwise variability indices (PVI) (Low et al., Reference Low, Grabe and Nolan2001).Footnote ³

These measures capture separately the degree of variability in the vocalic intervals (vowel duration, PVI-V, ∆V, etc.) and the intervocalic (consonantal) intervals (PVI-C, ∆C, etc.). They represent a reflection of the structural properties of the syllables. This seems to contradict the common assumption of isochrony theory, according to which the syllabic unit is the important element at the basis of rhythmic impression, although, of course, there are measures also based on variability in syllable duration.

Languages cannot be classified solely based on isochrony measures. However, they can be classified using both Ramus’ and Low’s structural measures (Ramus, Reference Ramus, Nespor and Mehler1999; Grabe and Low, Reference Grabe, Low, Gussenhoven and Warner2002; etc.). These measures visualize the stress-timed–syllable-timed continuum, identifying vocalic and consonantal dimensions along which any language might deviate from the prototypical rhythm in a sort of rhythm space (see Barry and Andreeva, Reference Barry, Andreeva and Russo2010).Footnote ⁴ These rhythmic measures capture various aspects of syllable complexity, making them suitable for comparing languages. These differences in complexity also impact the time required for articulating a syllable. These variability-based rhythm measures (PVI, ∆C, and ∆V) capture durational differences between consecutive vocalic and intervocalic intervals, which are correlated with differences in syllabic structure and the durational effects of degrees of prominence. However, rhythm measures, which focus on variability rather than isochrony (i.e., on the durational consequences of differences in syllable structure and phrasal modification), exhibit a less obvious connection between auditory impressions and physical measures (Barry and Russo, Reference Barry, Andreeva, Russo, Dimitrova and Kostadinova2003; Barry et al., Reference Barry, Andreeva, Russo, Dimitrova and Kostadinova2003; Asu and Nolan, Reference Asu and Nolan2005; Dellwo, Reference Dellwo, Karnowski and Szigeti2006; Mok and Dellwo, Reference Mok and Dellwo2008). Arvaniti (Reference Arvaniti2009) pointed out that there are no objective criteria for postulating a convincing degree of proximity or distance between measures to support a grouping or separation of languages. It’s important to acknowledge the challenge of relating recent rhythmic measures to any auditory perception of rhythm. As a result, we must question whether languages can truly be reliably differentiated based on such measures (Arvaniti, Reference Arvaniti2009; Barry et al., Reference Barry, Andreeva and Koreman2009; Russo and Barry, Reference Russo, Barry and Russo2010; Chapter 30).

46.1.2 Speech Rhythm and the Speech Rhythm Space in Stuttering

Speech impairments can have an impact on rhythm. This is particularly evident in the case of stuttering, a motor control disorder that affects 1% of the global population (Yairi and Ambrose, Reference Yairi and Ambrose2013). Stuttering speech is characterized by the presence of disfluencies, including repetitions of segments/sounds, syllables, words, prolongations of sounds, and interruptions (silent blocks), which can also manifest themselves as a glottal stop in the pre-phonatory posture (Guitar, Reference Guitar2013; Monfrais-Pfauwadel, Reference Monfrais-Pfauwadel2014; Onslow, Reference Onslow2020). Consequently, predictive rhythmic timing is malfunctioning in stuttering children, adolescents, and adults, since their ability in rhythmic speech production and timing is compromised.

People who stutter know exactly what they want to say but are temporarily unable to articulate their speech due to muscle contractions. This sets them apart from non-stuttering individuals who also produce disfluencies, which are more reflective of lexical search or lexical planning time (Lickley, Reference Lickley, Bertini, Celata, Lenoci, Meluzzi and Ricci2018). Being hindered from producing their speech can lead to negative feelings in people who stutter, such as frustration or embarrassment, to the point where speakers may fear speaking up, avoid eye contact with their interlocutor, and/or isolate themselves.

There are two types of stuttering: developmental stuttering and acquired stuttering. Developmental stuttering typically begins between the ages of two and seven and disappears in 80% of cases. Acquired stuttering is a generic term for all stutters that are not developmental. It can be caused by a stroke, tumour, head injury, side effects of certain medications, and so on (Guitar, Reference Guitar2013).

In this chapter, we will focus solely on developmental stuttering. Its origin is multifactorial, involving neurological (Etchell et al., Reference Etchell, Civier, Ballard and Sowman2018) and genetic factors (Riaz et al., Reference Riaz, Steinberg and Ahmad2005; Domingues and Drayna, Reference Domingues and Drayna2015). Apart from these aspects, stuttering is also influenced by several linguistic and/or phonetic factors (Howell et al., Reference Howell, Au-Yeung and Sackin1999; Au-Yeung et al., Reference Au-Yeung, Vallejo Gomez and Howell2003; Buhr and Zebrowski, Reference Buhr and Zebrowski2009). This speech disorder primarily affects the first syllable at the beginning of a turn-taking (Monfrais-Pfauwadel, Reference Monfrais-Pfauwadel2014). Lexical words such as nouns and verbs tend to be more disfluent in adults who stutter compared to functional words. Similarly, stressed syllables are more difficult to pronounce for people who stutter compared to unstressed syllables. Moreover, typical stuttering disfluencies have the peculiarity of being able to break within syllables instead of occurring between syllables or words. They are usually accompanied by tension that may be audible.

Stuttering has an impact on the timing and rhythmic flow of production since it affects timing mechanisms (Guitar, Reference Guitar2013; Monfrais-Pfauwadel, Reference Monfrais-Pfauwadel2014; see DSM-5 in Crocq et al., Reference Crocq, Guelfi, Boyer, Pull and Pull-Erpelding2015; Didirková et al., Reference Didirková, Le Maguer and Hirsch2021).Footnote ⁵ We know, for example, that the oro-laryngeal timing of people who stutter has particular characteristics. In particular, voice onset time (VOT) and voice termination time (VTT) are longer in this category of speakers than in people who do not stutter (Agnello, Reference Agnello, Webster and Furst1975). Other research, based on electromagnetic articulography (EMA) data, has shown breaks in articulatory timing at the supraglottal level (Didirková et al., Reference Didirková, Le Maguer and Hirsch2021). Stuttering demands a temporal adaptation from speakers when synchronizing rhythmical movements to provide a structural grid of regularity and recurrence. Stuttering affects notably the background of regularity (i.e., the underlying rhythm of speech), the sequences of evenly spaced phonetic material, matched segments, and syllables. Thus, it can be defined as a neurodevelopmental disorder that disrupts the temporal organization of speech. Monfrais-Pfauwadel (Reference Monfrais-Pfauwadel2014: 2) speaks of audible and perceptible traces of motor and then psychic struggle in contrast with normal speakers. This is consistent with accounts of interruptions (freezing) in stuttering (see Assaneo and Poeppel, Reference Assaneo and Poeppel2018; Alm, Reference Alm2021; Orpella et al., Reference Orpella, Flick and Assaneo2024 on the interaction between auditory and speech-motor cortices, and the synchronization between auditory and speech-motor regions related to speech rates). Orpella et al. (Reference Orpella, Flick and Assaneo2024) suggest that there is a reactive inhibitory control response from stutterers when they produce a word that will likely be stuttered. Technically, persons who stutter (PWS) show deactivation of left-hemisphere sensorimotor structures and overactivation of right-hemisphere parts. The problem is due to a lack of motor integration to regulate the movements of speech.

Studies on PWS have evaluated the speech on rhythmic measures such as rate. They have already shown that people who stutter do not use a typical tempo in speech and do not have a rhythmic speech (Boecher et al., Reference Boecher, Franich and Usler2022). PWS have specific patterns in perceptually non-fluent speech, mainly characterized by a lack of coordination between supraglottal articulations and laryngeal gestures; they have a longer laryngeal movement reaction, compared with fluent speech produced by persons who do not stutter (PWNS) (Zimmermann, Reference Zimmermann1980; Van Lieshout et al., Reference Van Lieshout, Hulstijn and Peters1996; Max and Gracco, Reference Max and Gracco2005; Heyde et al., Reference Heyde, Scobbie, Lickley and Drake2016; Didirková et al., Reference Didirková, Le Maguer and Hirsch2021). This affects the temporal variability of oral articulations and the speech rate. A longer duration of onset movements than in PWNS, closing gestures, complex consonant clusters, or vowel nuclei are encouraged by the steady position of the phonatory system, lips, or jaws. In PWS some articulatory movements show high velocity despite lower tempo, a negative correlation that could reflect defective speech–brain synchronization; in line with this proposal, the brains of PWS seem to exhibit alterations, resulting in less stable speech-motor planning and execution (Alm, Reference Alm2004, Reference Alm2021; Alario et al., Reference Alario, Chainay, Lehericy and Cohen2006). These factors lead PWS to asynchronous movements and a variable articulatory behaviour. Consequences of this are the stuttering-like disfluencies mentioned above and the difficulties for PWS to increase their speech rate (Howell et al., Reference Howell, Au-Yeung and Sackin1999). Thus, PWS show a poor temporal coordination, variable gestural movements, and a dysfunctional inter-articulatory coordination (Didirková et al., Reference Didirková, Le Maguer and Hirsch2021).

The aim of this chapter is to study how rhythm is disrupted in stuttering speech by comparing adults who stutter (PWS) with typically developing adults (PWNS) (n = 14 per group). We assess simple and complex rhythmic chunks to achieve this. Speech rhythm has been quantified using rhythmic measures (the PVI from Grabe and Low, Reference Grabe, Low, Gussenhoven and Warner2002; ∆V and ∆C from Ramus, Reference Ramus, Nespor and Mehler1999; Ramus et al., Reference Ramus, Nespor and Mehler1999). Both PVI and ∆V/∆C provide a diagnostic frame for identifying the two-dimensional presentation of the values (vocalic or consonantal) along which stuttered speech deviates from the prototypical normal speech.

46.1.3 Methods

Among the types of stuttering described in the literature (Yairi and Ambrose, Reference Yairi and Ambrose2013; Ward, Reference Ward2018), our study deals with persistent developmental stuttering, which generally starts between ages three and seven and remains persistent from adolescence to adulthood (Didirková et al., Reference Didirková, Le Maguer and Hirsch2021). A corpus study was conducted, and the raw and normalized pairwise variability index (nPVI) was computed for individual utterances to distinguish PWS from PWNS. We quantified rhythmicity in the speech of PWS using PWNS as control subjects.

46.1.4 Corpus and Participants

Our corpus investigation is based on audiovisual recordings coming from the French ANR project under grant no. ANR-18-CE36–0008 (BENEPHIDIRE: Bégaiement: la Neurologie, la Phonétique, l’Informatique pour son Diagnostic et sa Rééducation, PI: Fabrice HirschFootnote ⁶). The main objective of this ANR project is to enhance our understanding of stuttering to facilitate diagnosis and treatment of this disorder by speech-language therapists. To collect the data, a multidisciplinary team, composed of researchers in linguistics, computer scientists, neurologists, along with therapists specialized in treating this disorder, was assembled. This ANR project acquired morphological brain-imaging data, articulatory data using dynamic MRI, and acoustic data.Footnote ⁷ Our study is part of the work package aimed at studying the acoustic and motor characteristics of disfluencies.

We analysed data from 28 French native speakers (14 males and females who stutter; 14 males and females who do not stutterFootnote ⁸), who participated in an interview task conducted by a speech pathologist (a phoniatrist), accompanied by a speech therapist. The French treatment model is based on a relaxed style of interacting and motor rehabilitation in order (1) to reduce avoidance of speaking and (2) to develop gradually normal speech and to eliminate negative feelings. The therapy aims at restoring flexible and spontaneous speech that allows patients to express themselves even when disfluencies persist in conversational settings with smooth transitioning between listening and speaking.

The participants were asked by a phoniatrist to perform several tasks in the way of semi-directed speech and reading. The semi-spontaneous speech focuses on the description of a typical day, hobbies, Covid period and life, the emotional experience of the person with stuttering. Participants (PWS and PWNS) completed the interview task and a reading passage during the same interview under clinical test-taking conditions. Thus, PWS and PWNS participants were engaged in an in-person conversation. Control participants completed the same tasks and reading passage as PWS and matched the same questions. We measured for each speaker 13 minutes of the interview task, distributed over nine minutes of semi-spontaneous speech and four minutes of the reading task. The task also included a syllable-timed speech test as a training device aimed at enhancing speech in PWS. With this device, PWS were pushed to produce their speech with more isochronous intervals. The stuttering of our PWS was evaluated as severe by their speech therapist on the Riley’s Stuttering Severity Instrument scale (Riley, Reference Riley1994).

In the following sections we show the analysis conducted on four speakers (two per group PWS and PWNS).Footnote ⁹ We extracted 229 spontaneous speech samples from the recorded interviews. All samples were longer than four syllables (ips = inter-pause stretches > four syllables), matched for length and tasks in terms of PWNS. The ips are the utterance units used for calculating individual rhythm measures, which are then grouped and averaged over speakers. In addition to spontaneous speech, a total of 79 read speech samples were extracted.

46.2.1 Rhythm Measurements and the PVI: A Quantitative Analysis

We highlight that the rhythmic measures introduced in Section 46.1, such as PVI-V and ∆V, reflect differences between languages with only single-slot syllabic nuclei and those with single and double-slot nuclei. Additionally, PVI-C and ∆C measures are sensitive to differences in onset and coda structure (see Russo and Barry, Reference Russo, Barry and Russo2010). Thus, a language with more variable onset and coda structure, long and short vowels, and a reduction of unstressed syllables will generate higher variability measures than a language without such features. Furthermore, it’s important to note that the same rhythmic measures, calculated for the same language but from two different corpora, can result in radically different typological associations in terms of rhythmicity.

We calculated the variability of vowel and consonantal duration and computed rhythmicity of the PWS utterances, adopting the PVI methodology first proposed by Grabe and Low (Reference Grabe, Low, Gussenhoven and Warner2002) to measure rhythmic duration. The basic hypothesis to be tested is that the range of vowel variability, consonantal duration, possible syllable complexity, and other phonological differences between PWS and PWNS lead to an important difference in the rhythm measures between groups. Quantified rhythmicity thus depends on the intersection of multiple parameters, and it is defined in terms of degree rather than rhythmic dichotomy (see Barry and Andreeva, Reference Barry, Andreeva and Russo2010).

The rhythmic nature of the speech alterations in stuttering also leads us to some understanding of the cognitive phonological processes behind the behavioural PWS data. We conducted measurements on vocalic intervals and the intervals between vowels (excluding ‘normal’ disfluencies, such as pauses and hesitations) within a speech passage. We calculated the PVI, the mean difference in vocalic and intervocalic intervals from one vowel or one consonantal interval to another (raw PVI (rPVI) and normalized PVI (nPVI)).

This index of variation quantifies the extent of variability observed in consecutive measurements. Equation (1) provides the rPVI.

1. (1)

   $\mathrm{\text{rPVI}}=\left(1/(m-1)\right)\times {\sum }_{k=1}m-1\left|d_k-d_{k+1}\right|$

where m is the number of intervals and d_k is the duration of the k-th interval. In (1), rPVI (PVI-C) is not normalized for speech rate. However, a normalized PVI, which relates the difference between intervals to the mean duration of the two intervals, was introduced by Deterding (Reference Deterding2001) (cf. Low et al., Reference Low, Grabe and Nolan2001) as an explicit correction for tempo change.Footnote ¹⁷ Thus, speech rhythm was quantified in our study between successive vowels also using the nPVI. This nPVI version is represented by the following equation in (2) (see Deterding, Reference Deterding2001; Barry and Russo, Reference Barry, Andreeva, Russo, Dimitrova and Kostadinova2003; Russo and Barry, Reference Russo and Barry2008a, Reference Russo and Barry2008b, Reference Russo, Barry and Russo2010; among others).

1. (2) nPVI

   $\mathrm{n}\mathit{PVI}=100\times \left[\sum _{k=1}^{m-1}\left|\frac{d_k-d_{k+1}}{\left(d_k+d_{k+1}\right)/2}\right|/\left(m-1\right)\right]$

In (2), the duration (d) of a vowel (k + 1) is subtracted from the duration of the preceding vowel (k) and divided by their average duration. The absolute values of the resulting subtotals are summed up and divided by the number of vowels in the phrase (m) minus one. The result is multiplied by 100 to obtain a normalized score.

The normalization method used for PVI-V (but not for PVI-C) shows that the nPVI-V reduces local inter-syllabic differences, such as stressed versus unstressed or short versus long vowel, which are essential cues of rhythmic impressions. Thus, the range of vowel variability is generally reduced in comparison to Ramus measures (∆V and ∆V; see Section 46.3), but, as will be shown below, its sensitivity to tempo effects remains.

PVI-V captures the degree to which consecutive vowel durations vary: (a) long versus short vowels, b) phonetic variation due to differences in degree of aperture – the effects of phrasal accentuation. The PVI-C captures, on the other hand, the degree to which consecutive consonantal durations vary (e.g., single consonants or consonant clusters). PVI-V and PVI-C provide a measure of variation that takes the sequential nature of rhythmic impressions into consideration. The PVI-C measure captures the degree to which consecutive consonantal durations vary (i.e., single consonants or clusters).

We performed all the calculations of rPVI and nPVI for vocalic and intervocalic intervals using R Core Team (2021). Vocalic intervals were identified as the portion of the signal between the onset and offset of a vowel, characterized by vowel formants. This definition encompassed sections with varying numbers of vowels, including monophthongs or multiple vowels spanning across the transition between adjacent words.

Intervocalic intervals, on the other hand, were defined as the segment of the signal between the offset of one vowel and the onset of the subsequent vowel, regardless of the number of intervening consonants. To measure the duration of both vocalic and intervocalic intervals, we employed a left-to-right approach using wideband spectrograms in Praat. Our first query was whether r/nPVI were different between PWS and PWNS. The higher values of rPVI-C (as for ∆C) indicate that PWS speech is sensitive to complex consonantal structures in the onset and coda of a syllabic structure, as commonly found in languages with a predominant C(C)VC(C) structure.

To offer a broader perspective and some possibility for comparison, we calculated (in Section 46.3) both the sequentially calculated pairwise variability measures (PVI) using the pairwise normalization procedure for vowels (Grabe and Low, Reference Grabe, Low, Gussenhoven and Warner2002) and the three global measures used by Ramus et al. (Reference Ramus, Nespor and Mehler1999) applied to both stuttering and normal speech and the speech rate (the number of vowel intervals per second, including pauses) (3).

1. (3) Ramus measures

   %V (within ips = inter-pause stretches)

   ∆V – standard deviation of vocalic intervals

   ∆C – standard deviation of intervocalic intervals

The variability of vocalic and intervocalic (consonantal) intervals are taken by both Ramus and by Grabe and Low as correlates of the complex interaction of structural properties. However, their way of calculating the variability is different. The Ramus %V measure does not capture variation, and ∆V and ∆C are measures of overall vocalic and consonantal variation rather than an accumulative pairwise measure. The vocalic proportion of the utterance (%V), as a measure, is difficult to interpret in connection with any concept of rhythm.

46.3.1 Speech Rhythmicity of Individual Speakers

We found systematic differences between two speakers who stutter; thus, clearly, personal speech production strategies of PWS affect ‘rhythm’ measures. The range of PVI values produced by the same speaker was greater in the PWS group compared to the PWNS group, whereas PWNS produced a more regular nPVI level. We show in Figure 46.12 the average duration of speech components at the individual speaker level, incorporating both normal and stuttered speech, categorized according to different contextual factors such as vocalic and intervocalic intervals, pauses, and hesitation, with error bars indicating the SEM.

Figure 46.12

Average duration metrics for vocalic and intervocalic intervals, pauses, and hesitations among individual speakers.

Duration of speech components among individual speakers. The bar graph compares the average duration of vocalic and intervocalic intervals, pauses, and hesitations for individual speakers identified as 00001bis, B31, C14, and C15. It highlights the contrast in speech durations between normal speakers and PWS, with PWS generally showing longer durations in pauses and hesitations, indicative of the speech disfluencies commonly associated with stuttering. The differences in duration and variability between speakers underscore the individual nature of stuttering manifestations.

Figure 46.12 Long description

The vertical axis represents duration, which ranges from 0 through 600 milliseconds. The horizontal axis lists the following: Vocalic, Intervocalic, Pause, and hesitation. The bars represent Normal and Stutter, respectively. The mean duration values for Stutter are as follows. First: Pause, 350. Hesitation, 220. Vocalic, 170. Intervocalic, 110. Second: Pause, 500. Hesitation, 400. Intervocalic, 120. Vocalic, 80. The mean duration values for Stutter are as follows. Third: Hesitation, 300. Pause, 290. Vocalic, 90. Intervocalic, 80. Fourth: Hesitation, 460. Pause, 150. Intervocalic, 100. Vocalic, 90. The values are estimated.

Figure 46.12 suggests that for some speakers, the duration of vocalic intervals is longer in stuttered speech compared to normal speech, although this is not consistent across all speakers. However, the figure shows that the intervocalic intervals (rPVI) can be longer for stuttered speech than for normal speech. The duration of pauses appears to be longer in stuttered speech for all individual speakers represented in the figure, suggesting a common trait among PWS. The average duration for hesitations is longer for stuttered speech, while in others, it is comparable between stuttered and normal speech. The error bars across all categories indicate variability in the durations for both normal and stuttered speech. A larger error bar indicates for PWS more variability in that particular speech component. Overall, the figure reveals that the average duration of both intervocalic intervals and pauses is longer in stuttered speech compared to normal speech, with individual variations across the speakers. Hesitations and vocalic intervals show a less consistent pattern and may vary more on an individual basis.

Figure 46.13 illustrates the mean duration of various speeches (normal and stuttered) within the four contextual categories across distinct speech styles (read and spontaneous), with error bars representing the SEM, which reflects the amount for each speaker.

Figure 46.13

Comparative analysis of speech for individual speakers. Mean durations for vocalic intervals, intervocalic intervals, pauses, and hesitations.

Detailed speech component durations by speaker. Presenting both mean values and variability, this dual-bar graph compares the duration of vocalic and intervocalic intervals, pauses, and hesitations for each of the four speakers distinguished by ‘normal’ and ‘stutter’ speech patterns. Inter-speaker variability is marked, with PWS often showing extended durations and higher variability in pauses and hesitations. The graph also illustrates significant inter-speaker variability, particularly in the stuttering group, underscoring the personalized nature of speech disruptions experienced by PWS.

Figure 46.13 Long description

The vertical axis represents duration, which ranges from 0 through 600 milliseconds. The horizontal axis lists the following: Vocalic, Intervocalic, Pause, and hesitation. The bars represent Normal and Stutter, respectively. The mean duration values for Stutter are as follows. First: Pause, 350. Hesitation, 220. Vocalic, 170. Intervocalic, 110. Second: Intervocalic, 210. Vocalic, 80. Third. Pause, 430. Hesitation, 220. Vocalic, 130. Intervocalic, 100. Fourth. Pause, 500. Hesitation, 400. Intervocalic, 110. Vocalic, 80. The mean duration values for Normal are as follows. First: Vocalic, 100. Intervocalic, 100. Pause, 100. Second. Vocalic, 80. Intervocalic, 100. Pause, 300. Third: Pause, 400. Hesitation, 400. Pause, 300. Vocalic, 100. Intervocalic, 100. Fourth: Hesitation, 480. Pause, 100. Intervocalic, 100. Vocalic, 100. The values are estimated.

PWS often have increased mean durations for pauses and hesitations, as this is a common feature of stuttered speech. This is typically seen in the higher dark grey bars within these categories. The standard error represented by the error bars is larger for PWS in certain speech elements, such as pauses and hesitations. This is indicative of greater variability in how these elements are expressed by PWS compared to PWNS.

By comparing read and spontaneous speech, PWS show a larger discrepancy in mean durations between these two types of speech, reflecting the increased challenges PWS face in spontaneous speech scenarios.

46.3.2 Speech Rhythmicity in PWS

We calculated also the percentage of vocalic intervals, intervocalic intervals, pauses, and hesitations during stuttering speech and normal speech for individual speakers; see Table 46.2.

In Table 46.2, ∆C and ∆V do not separate PWS from PWNS; however, stuttering disfluencies are numerically important. This result is not surprising since Ramus’ ∆V measure captures global variation within ips, whereas Grabe and Low’s PVI measure captures ‘pairwise’ sequential variation. We calculated the average and the standard deviation of vocalic intervals, intervocalic intervals, duration of pauses, and duration of hesitations. The numerical results in Table 46.2 are also visible in Figure 46.14.

Figure 46.14

Standard deviation of vocalic intervals, intervocalic intervals, duration of pauses, and duration of hesitations.

Variability in speech components among individual speakers. The graph displays the standard deviation of vocalic and intervocalic intervals, pauses, and hesitations for two PWNS (C14 and C15) and two PWS (00001bis and B31). The pronounced variability in the stuttering speakers’ intervocalic intervals and hesitations, particularly for B31, indicates the degree to which stuttering can affect speech rhythm and flow.

Figure 46.14 Long description

The vertical axis represents duration, which ranges from 0 to 600 milliseconds. The horizontal axis lists the following: Vocalic, Intervocalic, Pause, and hesitation. The bars represent Normal and Stutter, respectively. The values of standard deviation for Stutter are as follows. Top Left. Pause, 300. Vocalic, 200. Hesitation, 190. Intervocalic, 50. Top right. Hesitation, 600. Intervocalic, 210. Pause, 200. Vocalic, 50. Bottom left. Pause, 200. Hesitation, 110. Vocalic, 90. Intervocalic, 20. Bottom right. Vocalic, 80. Intervocalic, 80. Pause, 80. Hesitation, 10. The values are estimated.

In PWNS the standard deviation values across all speech elements are relatively low. For the PWS, the standard deviation values are also relatively low (but higher than PWNS) and comparable to those of the normal speakers (but higher than PWNS for intervocalic intervals). A PWS speaker has a noticeably higher standard deviation in the hesitation category, which suggests a significant variability in the duration of hesitations. This could indicate moments where the speaker is experiencing blocks or is attempting to avoid disfluent moments. The standard deviation for intervocalic intervals for PWS speaker B31 shows a much larger standard deviation compared to both the normal speakers and PWS speaker 00001bis. This suggests that there is a greater variability in the timing of consonantal intervals for this speaker, which may reflect the irregular speech rhythm commonly associated with stuttering. This could be related to the stuttering disfluencies that affect the flow of speech, potentially leading to more pronounced and irregular spacing between consonantal sounds. This can be a characteristic of the speech patterns in PWS.

Figure 46.15 displays the standard deviation of speech elements for both normal speakers and PWS, broken down by read and spontaneous speech styles. Both speakers exhibit low standard deviation in read and spontaneous speech styles across all speech elements. This suggests that their speech timing is fairly consistent, whether reading a text or speaking spontaneously. For PWS, during spontaneous speech, the standard deviation increases, especially for intervocalic intervals and hesitations. In a speaker who stutters (B31) during spontaneous speech, there’s a notable increase in the standard deviation for hesitations. This large variability could indicate significant disruptions in speech flow due to stuttering, affecting the speaker’s ability to maintain consistent hesitations. Overall, the figure illustrates that while normal speakers maintain a consistent rhythm across both speech styles, PWS exhibit more variability, particularly in spontaneous speech. This is most pronounced in the duration of their hesitations, suggesting that spontaneous speech poses more significant challenges for individuals who stutter.

Figure 46.15

Standard deviation of vocalic intervals, intervocalic intervals, duration of pauses, and duration of hesitations. Effect of speech style: read and spontaneous.

Speech component variability across speaking conditions for individuals. In this graph, intervocalic intervals demonstrate notable variability for PWS, especially in spontaneous speech, with speaker B31 showing a heightened standard deviation in intervocalic intervals during spontaneous speech. This suggests that the timing between spoken sounds is a critical indicator of stuttering, highlighting the irregular speech rhythm and flow for PWS. Additionally, the variability in pause and hesitation durations for PWS further emphasizes the rhythmic disruptions characteristic of stuttering.

Figure 46.15 Long description

The vertical axis represents duration, which ranges from 0 to 600 milliseconds. The horizontal axis lists the following: Vocalic, Intervocalic, Pause, and hesitation. The bars represent Normal and Stutter, respectively. The values of standard deviation for Stutter are as follows. First. Pause, 300. Vocalic, 200. Intervocalic, 80. Hesitation, 10. Second. Pause, 450. Vocalic, 200. Hesitation, 200. Intervocalic, 80. Third. Intervocalic, 200. Vocalic, 20. Pause, 10. Hesitation, 10. Fourth. Hesitation, 600. Intervocalic, 210. Pause, 200. Vocalic, 50. The values of standard deviation for normal are as follows. First. Vocalic. Intervocalic, 50. Pause, 10. Hesitation, 10. Second. Vocalic, 100. Hesitation, 100. Intervocalic, 50. Pause, 50. Third. Intervocalic, 50. Vocalic, 40. Pause, 10. Hesitation, 10. Fourth. Vocalic, 80. Intervocalic, 80. Pause, 50. Hesitation, 10. The values are estimated.

The comparison of stuttering and normal speech rhythm in read and spontaneous conditions for the PVI measures is shown in Figure 46.16, where the points represent the results of all utterances. Vocalic nPVI values are plotted on the vertical axis against intervocalic rPVI values on the horizontal axis.

Figure 46.16

PVI results for all utterances.

PVI values by individual and speech context. This scatterplot maps the vocalic nPVI against the intervocalic rPVI for four speakers, distinguishing between ‘normal’ and ‘stutter’ speech patterns during read and spontaneous speaking tasks. For both 00001bis and B31, who stutter, there is a noticeable spread in intervocalic rPVI values, particularly in spontaneous speech, indicating substantial rhythmic variability.

Figure 46.16 Long description

The horizontal axis represents intervocalic r P V I which ranges from 50 to 150. The vertical axis represents vocalic n P V I which ranges from 25 to 75. Top left and right. The data points representing read stutter and spontaneous stutter are randomly distributed throughout both graphs. Bottom left and right. The data points representing read normal and spontaneous normal are randomly distributed throughout both graphs. The legends for these data points are given on the right side.

In Figure 46.16, individual utterance measures are plotted for both normal speakers and PWS across two speech conditions: read and spontaneous. The PVI is used to analyse the rhythmic characteristics of speech, with vocalic nPVI on the vertical axis indicating variability between vowel durations, and intervocalic rPVI on the horizontal axis indicating variability between consonant durations. It is well known that the selection of words, and the prosodic structure of the utterances at phrasal level, can result in considerable shifts in values. The tempo and style of speech (e.g., the difference between read and spontaneous speech, or the type of read text or the type of natural discourse) influenced the values that have been obtained. However, there are key elements to identify PWS and understand the rhythmic measures of nPVI and especially rPVI. The spread of the points along the horizontal axis (rPVI) shows variability in the timing of intervocalic intervals. We can observe variance of PVI values at intra-speaker level across utterances in the spontaneous speech interview. PWS have points that are spread further right, and this suggests higher variability in their intervocalic rhythmic measure compared to normal speakers. One can observe how the speech rhythm changes from a read to a spontaneous condition. A significant shift in position, especially horizontally, indicates how stuttering impacts speech in a less controlled environment.

In Figure 46.17 we show the average of the PVI results for the comparison of stuttering and normal speech rhythm in read and spontaneous conditions. Vocalic nPVI values are plotted on the vertical axis against intervocalic rPVI values on the horizontal axis.

Figure 46.17

PVI results: average.

Vocalic and intervocalic temporal variability in speech. This scatterplot contrasts vocalic nPVI and intervocalic rPVI across read and spontaneous speech tasks for each speaker, showcasing the temporal dynamics of speech. The plot points to differences between normal speakers (C14 and C15) and those who stutter (00001bis and B31), particularly in spontaneous speech. The individuals who stutter demonstrate wider scatter in intervocalic rPVI values, indicative of the variability in speech rhythm and timing that is characteristic of stuttering.

Figure 46.17 Long description

The horizontal axis represents intervocalic r P V I which ranges from 40 to 120. The vertical axis represents vocalic n P V I which ranges from 46 to 58. Top left and right. The data points representing read stutter and spontaneous stutter are randomly distributed throughout both graphs. Bottom left and right. The data points representing read normal and spontaneous normal are randomly distributed throughout both graphs. The legends for these data points are given on the right side.

In Figure 46.17, the average PVI results are plotted for individuals with normal speech and PWS across two different speech conditions: read and spontaneous. Higher nPVI values on the vertical axis indicate greater variability in vocalic intervals. In this figure, the nPVI values for all speakers seem to fall within a relatively close range, suggesting similar variability in vocalic intervals among the speakers. Higher values on the horizontal axis indicate greater variability in intervocalic timing. PWS have higher rPVI values than normal speakers; this indicates more pronounced variability in their intervocalic interval.

Normal speakers exhibit less variability in both nPVI and rPVI, indicating more regular speech rhythm, while PWS show more variability, particularly in rPVI, indicating a less regular rhythm. The PVI values, particularly the intervocalic rPVI, could be used to identify and characterize the rhythmic differences in speech related to stuttering. However, in spontaneous speech, we expect a considerably higher degree of variation in measures due to both the material being spoken and the individual characteristics of the speaker. We have observed that personal speech production strategies during stuttering affect rhythm measures. There will always be differences between PWS with different phonotactics and differences in durational oppositions. However, PWS demonstrate considerable variability in their speech rhythms, particularly exhibiting greater variability in the intervocalic intervals (consonants) measured by PVI-C (and ∆C), compared to PWNS.

47.3.1 Participants

Twenty-eight children and adolescents participated in the current study, 14 with an ASD diagnosis and 14 typically developing peers between the ages of nine and 15 years. The participants with ASD and the neurotypical (NT) peers were matched on age (ASD: M = 12.47, SD = 1.9; NT: M = 12.53, SD = 1.9), gender (three girls and 11 boys in both groups), and nonverbal IQ (ASD: M = 107, SD = 10.8; NT: M = 110, SD = 8.85), but differed significantly in composite IQ scores (F(1,22) = 5.11, p = .03) and language functioning (F(1,22) = 18.08, p < .001). All ASD participants had received a medical diagnosis of ASD (Autism = 7, High Functioning Autism = 5, Asperger’s Syndrome = 1, PDD-NOS = 1) prior to participating in this study, according to parent report, and received educational services due to their ASD diagnosis at the time of the study. The mean age of ASD diagnosis was 4.2 years with a range from two to eight years. All participants in the ASD group were administered the Autism Diagnostic Observation Schedule, Second Edition (ADOS-2) (Lord et al., Reference Lord, Rutter and Dilavore2012), a semi-structured standardized assessment widely used to assess and diagnose ASD as part of this study. Only participants with ASD who met the ADOS-2 cutoff score for autism or autism spectrum were included in this study. Since all participants with ASD were fluent speakers of English, Module 3 of the ADOS-2 (Fluent Speech – Child/Adolescent) was administered to all ASD participants.

This study was approved by the New Mexico State University Institutional Review Board and conforms with the guidelines of the Office of Research Integrity and Ethics. The legal guardians of the participants provided written informed consent, and the participants themselves provided written assent to participate in this study. All participants were recruited from the Southern New Mexico area – a linguistically and culturally diverse region of the United States. Of the 14 participants with ASD, seven were of Hispanic heritage, six were Caucasian, and one was African American. Ten of the neurotypical peers were Hispanic and three were Caucasian. However, all participants met the inclusion criteria of coming from households where English was spoken as the first and primary language. All participants passed hearing screenings at pure-tone frequencies of 500 Hz, 1,000 Hz, 2,000 Hz, and 4,000 Hz, and reported normal vision. All participants were administered the Core Language portion of the Clinical Evaluation of Language Fundamentals, Fifth Edition (CELF-5; Wiig et al., Reference Wiig, Semel and Secord2013) to assess general language functioning. Furthermore, the Kaufman Brief Intelligence Test (KBIT-2; Kaufman and Kaufman, Reference Kaufman and Kaufman2004) was administered to determine verbal, nonverbal, and composite IQ scores. This assessment consists of three sub-tests, two measuring verbal IQ and one measuring nonverbal IQ performance. A summary of participant characteristics is shown in Table 47.1.

In order to meet inclusion criteria, participants were required to have nonverbal IQ standard scores of 85 or higher, a composite IQ standard score of 70 or higher, as well as a CELF Core Language standard score of 70 or higher in order to assure that participants were able to successfully engage in all study tasks.

As Table 47.1 shows, the two groups differ in terms of their verbal skills, as shown in the differences in verbal IQ and CELF-5 scores, which is to be expected given that challenges with verbal communication is one of the characteristics of autism. The difference in composite or total IQ, in turn, is entirely carried by the differences in verbal IQ between the two groups.

47.3.2 Conversational Task

The conversational task used in this study was the Diapix task (Baker and Hazan, Reference Baker and Hazan2011) – a goal-oriented conversational task that has been shown to elicit conversational prosodic entrainment and that has previously been used successfully with individuals impacted by communication disorders (Borrie et al., Reference Borrie, Lubold and Pon-Barry2015). Study participants were paired with an adult research assistant for the conversation. The research assistants were undergraduate students in the Communication Disorders program at New Mexico State University. A total of nine research assistants between the ages of 20 and 24 years were involved in the study. Research assistants were aware that the purpose of the study was the investigation of speech characteristics in youth with and without ASD, but they did not know that the aim was to investigate prosodic entrainment. All research assistants participated as conversation partners in conversations with participants from both groups but were not blinded to the ASD diagnosis of the study participants.

Each conversation partner was given a picture that was similar but not identical to the conversation partner’s picture. The conversational dyad was then asked to find 10 differences between their respective pictures through collaborative conversation without being allowed to see each other’s pictures. Research assistants and study participants alike were told that the goal of the conversational task was to find as many differences as possible. The conversation was ended after the dyad had identified the 10 differences between the pictures, or concurred that they were unable to find any further differences. All conversations were conducted in a sound-treated room and recorded in audio wave file format at a 44.1 kHz sampling rate with 16-bit resolution using a Marantz PMD 670 digital recorder and a Shure SM58 cardioid dynamic microphone that was placed between the conversation partners, approximately 30 inches from each speaker. All sound files were then transferred for post-processing and labelling. Audio files were annotated using Praat (Boersma and Weenink, Reference Boersma and Weenink2019) TextGrid files. All conversational utterances spoken during the conversation were labelled to indicate which of the two speakers had produced them. Only linguistically meaningful utterances were included. Nonlinguistic vocalizations such as laughter, humming, as well as speaker overlap were excluded from the subsequent acoustic analysis during which measures of speaking rate as words per minute (WPM) and mean F0 were extracted from all utterances. No significant difference in the mean duration of the conversations between the ASD (M = 8.1 minutes, SD = 1.74) and the NT (M = 7.6 minutes, SD = 1.94) group was present.

47.3.3 Calculation of Prosodic Entrainment

47.3.3.1 Speaking Rate

Several pre-processing steps were taken to prepare the data for analysis of speaking rate. More specifically, all acoustic recordings were down-sampled to 16 kHz and saved as a mono-channel PCM audio format. These steps ensured that our files would work with the various software tools throughout the experiment. Each audio file was then transcribed at the word level first using Azure speech-to-text and then hand-corrected. The results were stored in a separate text file along with the time, location, duration of each word. Using the Julia computing tool (Bezanson et al., Reference Bezanson, Karpinski, Shah and Edelman2017), each transcription was inserted into the appropriate TextGrid file as interval tier, so that each word had a start and an end time as determined by the offset and duration reported by the Microsoft Azure speech-to-text tool. Next, the following procedure was used to calculate entrainment in speaking rate:

1. 1. The WPM estimates were extracted for any segment produced by speaker 1 (S1). This segment, in turn, was surrounded by segments produced by speaker 2 (S2).

2. 2. The sign of the difference in speaking rate for the S1 utterance compared to the previous S2 utterance was calculated as: ∆S1 = sgn(S1 − S2Prev).

3. 3. The sign of the difference in speaking rate for the S2 utterance compared to the previous S2 utterance was calculated as: ∆S2 = sgn(S2Next − S2Prev).

4. 4. For each S1 segment, we multiplied ∆S1 by ∆S2.

The purpose of each of the aforementioned steps is as follows. Step 1 is responsible for extracting the three consecutive WPM readings for each segment required to perform our algorithm (first S2, S1, and second S2). The first S2 value is used as a reference value to calculate whether S2’s speaking rate has increased, decreased, or stayed the same after the S1 segment. Step 2 computes S1’s speaking rate and compares it to the first S2 segment. A value of 1.0 indicates that S1 increased speaking rate, a value of −1.0 indicates S1 decreased speaking rate, and a value of 0 indicates that the speaking rate stayed the same compared to first S2 segment. Step 3 computes S2’s speaking-rate entrainment using the same approach as described in step 2. Step 4 multiplies ∆S1 by ∆S2, which returns 1.0 if both values carry the same sign, that is, S2 is entraining to S1, and returns −1.0 if the two values carry different signs, that is, S2 is dis-entraining from S1, and if there was a change of less than 5 WPM, we considered this to signify neither entrainment nor dis-entrainment. This threshold of five WPM was chosen based on the analysis of samples from the corpus during which speakers produced two consecutive thematically unrelated turns with a pause between both turns but without turn exchange with an interlocutor. Therefore, we conclude that a five-WPM difference between turns is within the amount of variation that occurs naturally. An example of this four-step procedure where speaker 2 entrains to speaker 1 is provided in Figure 47.1.

Figure 47.1

Example of a turn exchange.

Speaker 2 entrains to Speaker 1. Step 1 shows the extracted WPM values of three consecutive WPM segments; Step 2 shows a value of 1.0 for ΔS1; Step 3 shows a value of 1.0 for ΔS2; Step 4 multiplies ΔS1 by ΔS2, resulting in a value of 1.0 indicating that S2 entrained to S1 during this turn exchange.

47.3.3.2 F0

47.3.3.2.1 F0 Entrainment Calculation at the Level of Conversational Turns

At the level of conversational turns, the average F0 value was extracted from each speaker segment using a Praat script that uses an autocorrelation-based method developed by Boersma (Reference Boersma1993) to estimate the pitch value every 10 ms automatically. Next, we used the Julia environment to average the F0 values for each segment produced by each speaker. The maximum allowable pitch range (pitch floor and pitch ceiling) used for the analysis was adjusted based on the speaker’s gender. Specifically, we used a pitch range of 70–250 Hz, 150–350 Hz, and 170–450 Hz for adult males, adult females, and children, respectively. The adult male range was used for the male adolescents in our dataset. In order to correctly classify each speaker as adult male, female, or child, a Julia function was developed to utilize the autocorrelation method developed by Boersma (Reference Boersma1993) to automatically estimate the mean F0 for each speaker. We next used the mean F0 estimate to select the appropriate F0 range for speakers based on their estimated F0 average.

Next, we used the same steps described in Section 47.3.3.1 to calculate F0 entrainment at the conversational turn level, using the extracted average F0 instead of WPM as the input to the algorithm. Any change in F0 that was greater than 5 Hz in the same direction as the interlocuter was considered an instance of entrainment, a change in F0 greater than 5 Hz in the opposite direction than that of the interlocuter was considered dis-entrainment, and changes of less than 5 Hz were considered neither entrainment nor dis-entrainment. This threshold of 5 Hz was chosen based on the analysis of the mean F0 difference in a set of samples from the corpus during which speakers produced two consecutive thematically unrelated turns with a pause between both turns but without turn exchange with an interlocutor. Hence, we assume that a +/− 5 Hz difference is not induced by the F0 of the interlocutor but by naturally occurring variance in F0.

47.3.3.2.2 F0 Entrainment Calculation at the Conversational Level

In order to assess whether entrainment behavior at the level of the conversational turn is predictive of global conversational entrainment as derived when comparing the beginning and end of a conversation, we also correlated F0 entrainment at the turn level with global conversational entrainment. Global F0 entrainment was assessed using the procedure developed in Lehnert-LeHouillier et al. (Reference Lehnert-LeHouillier, Terrazas and Sandoval2020). The following is a summary of the procedure. Mean F0 was extracted from the utterances produced by each speaker during the first and the last third of the conversation, using Praat (Boersma and Weenink, Reference Boersma and Weenink2019). The same autocorrelation-based pitch estimation method described above was used to extract pitch estimates at intervals of 10 ms during the labelled sections of the sound file – in this case, the initial and final third of the conversation. Based on the extracted F0 values, the mean F0 for each of the speakers was calculated for the first and the last third of the conversation. The distance between the mean F0 values for both speakers was calculated for both the first and the last third of the conversation by subtracting the mean F0 of one speaker from that of the other speaker in the dyad. A decrease in distance between the initial and the final third suggests mean F0 entrainment, whereas an increase of this distance suggests dis-entrainment. An illustration of this procedure is provided in Figure 47.2.

Figure 47.2

Conversational mean F0 entrainment.

Illustration of the approach used to determine mean F0 entrainment exhibited during a conversation. Based on the F0 contours produced by S1 and S2 during conversational turns in the first and the last third of the conversation, mean F0 for each speaker was calculated for the respective thirds. The difference/distance between both speakers’ mean F0 was then calculated. If this difference/distance decreased from initial to final third, as shown in this figure, speakers showed entrainment. A common mean F0 was calculated for each conversational dyad. The difference/distance of each speaker from this common mean during the first and last third was then calculated to determine the contribution of each speaker to the overall entrainment in mean F0.

As can be seen in Figure 47.2, S1 in this hypothetical example contributes more to the conversational entrainment in mean F0 than S2. It is also possible that two speakers entrain much less than the hypothetical speakers in Figure 47.2, such that the speaker who contributes more to the mean F0 entrainment in such a conversation with less overall entrainment would contribute an absolute F0 change to the F0 entrainment that is smaller than that of S2 in our Figure 47.2. In order to account for such a situation and to capture both aspects – the amount of overall conversational mean F0 entrainment as well as the individual contribution of our study participants – the conversational mean F0 entrainment measure was normalized using the entrainment contribution measure. The normalized mean F0 entrainment measure was then used as the input to the statistical analysis.

47.3.4 Statistical Analysis

All statistical analyses were conducted using the R statistical computing environment (R Core Team, 2016) and the packages “tidyverse” (Wickham et al., Reference Wickham, Averick and Bryan2019), “lme4” (Bates et al., Reference Bates, Mächler, Bolker and Walker2015), and “cocor” (Diedenhofen and Musch, Reference Diedenhofen and Musch2015). Mixed-effects modelling was used to assess (1) the group differences in speaking-rate entrainment and F0 entrainment at the level of the conversational turn, and correlation statistics were used to assess (2) the relationship between entrainment at the level of the conversational turn and global conversational entrainment. The first question was investigated by fitting a mixed-effects model with turn-level entrainment as the outcome variable and with group (ASD versus NT) and entrainment type (Entrainment, Dis-entrainment, and No Change) as predictor variables. Participants and conversation partners were modelled as random effects. The second question was answered via correlation testing using Pearson’s correlation coefficient r.