2.1 Forced alignment and its advancement of phonetic research

With the help of readily-available forced-alignment programs, phonetic investigations of English have advanced further than those of any other language. Meanwhile, phonetic investigations of the Nordic languages, including Swedish, have lagged. To offer an example, we examined and coded – according to language researched – the 782 articles published between 2001 and 2020 in the Journal of Phonetics. The top three researched languages were English, German and French with 336, 71, and 65 articles, respectively. Swedish, the most-commonly researched Nordic language, had a mere 12 articles, followed by Norwegian with eight, Danish with four, and Icelandic with one. Proportionate to number of speakers, these languages are somewhat underrepresented. Finnish, a language with approximately 5 million speakers, had 19 articles; Arrernte, a language with approximately 4,000 speakers, had six articles. As an additional example, before the onset of the project to which this development is tied (Young Reference Young2019), only three variationist investigations had ever been conducted on Swedish (Gross et al. Reference Gross, Boyd, Leinonen and Walker2016; Kotsinas Reference Kotsinas, Johan, Ulf, Michael, Hillevi and Bo1994; Nordberg Reference Nordberg and Karl-Hampus1975). Among these three, only the first-listed study was acoustic-phonetic, relying on manual segmentation (personal conversation with Johan Gross, 2020). The latter two were based on data that was never phonetically segmented; rather, variants were perceptually coded and counted.

We believe a circular dynamic is at play. The low number of contemporary phoneticians engaged with research on Nordic languages ^{Footnote 3} has translated into few investments in forced alignment. In turn, this lack of investment has perhaps discouraged growth of the field. For English, the same feedback cycle may also be operating, albeit in the opposite direction. The early dominance of research on English has motivated the development of a high number of forced aligners, which has allowed the anglo-linguistic enterprise to be more prolific than ever.

The four main forced-alignment suites that circulate today were all trained on the English language. They are Forced Alignment and Vowel Extraction (FAVE-Align; Rosenfelder et al. Reference Rosenfelder, Fruehwald, Evanini and Yuan2011; Yuan et al. Reference Yuan, Ryant, Liberman, Stolcke, Mitra and Wang2013), ProsodyLab Aligner (Gorman et al. Reference Gorman, Howell and Wagner2011), LaBB-CAT Transcriber (Fromont & Hay Reference Fromont, Jennifer, Paul and Scott2012), and the Montreal Forced Aligner (McAuliffe et al. Reference McAuliffe, Socolof, Mihuc, Wagner and Sonderegger2017). FAVE-Align (formerly known as the Penn Forced Aligner, Yuan & Liberman Reference Yuan and Mark2008), ProsodyLab Aligner, and the Montreal Forced Aligner are modeled on American English. LaBB-CAT is modeled on New Zealand English.

As a very recent addition (and after the onset of the present study), the Montreal Forced Aligner began offering pre-trained acoustic models for Bulgarian, Croatian, Czech, French, German, Hausa, Korean, Mandarin, Polish, Portuguese, Russian, Spanish, Swahili, Swedish, Thai, Turkish, Ukrainian, and Vietnamese. These newer models are trained on read-aloud speech and require the use of the GlobalPhone dictionary (Schultz & Schlippe Reference Schultz, Tim, Nicoletta, Khalid, Thierry, Hrafn, Bente, Joseph, Asuncion, Jan and Stelios2014), which is proprietary and costs 600 euros to obtain (alternative pronunciation dictionaries cannot be used because phone coding within the models is opaque). Performance metrics have not yet been released for any of these newer models (see Section 2.4).

Other options are EasyAlign for Praat (Goldman Reference Goldman2011) and the BAS Speech Science Web Services (Kisler et al. Reference Kisler, Reichel, Schiel, Draxler, Jackl and Pörner2016). EasyAlign offers automatic transcription for French, Spanish, and Taiwan Min, and works only on Windows machines. It appears that a singular adaptation had been made for Swedish in 2007, but this adaptation has not been made available to the public, and performance metrics were not ever disclosed (Lindh Reference Lindh2007). BAS Speech Science Web Services has offered for quite some time a web-accessible interface called WebMAUS Basic for automatic transcription of Basque, Catalan, Dutch, English, Estonian, Finnish, Georgian, German, Japanese, Hungarian, Italian, Maltese, Polish, Russian, and Spanish. Recently and also after the onset of this project, Swedish was also added, but performance metrics have not been released on this either.

As has been discussed in the Introduction, the present study is not the first time that FAVE-Align has been adapted as an ‘untrained’ prototype for lesser-studied languages. DiCanio et al. (Reference DiCanio, Nam, Whalen, Bunnell, Amith and García2013) built an adaptation for Yoloxóchitl Mixtec, and Coto-Solano Solórzano (Reference Coto-Solano and Sofía2017) built a similar prototype for the endangered lan- guage Bribri and, later, Cook Islands Maori (Coto-Solano et al. Reference Coto-Solano, Nicholas and Wray2018). Strunk et al. (Reference Strunk, Schiel, Seifart, Nicoletta, Khalid, Thierry, Hrafn, Bente, Joseph, Asuncion, Jan and Stelios2014) built a language-general model and used it to align read-aloud and spontaneous Baura, Bora, Even, and Sri Lankan Malay. These aligners produced between poor and fair accuracy levels, likely due to the typological difference between them and the language(s) their respective aligners were trained on (mostly Indo-European). The present adaptation stands out from this group in that its accuracy performance is competitive with custom-trained aligners.

What this review aims to demonstrate is that the development of forced alignment programs has been solidly anglocentric and that the expansion to other languages has excluded Nordic languages until very recently. This curious exclusion motivated the present study and has likely motivated the recent addition of Swedish to MFA and WebMAUS Basic. The ensuing performance analysis will serve as a handy baseline benchmark for the eventual testing of these other Swedish adaptations, and the step-by-step instructions we provide will allow others to duplicate our adaptation for other Nordic languages.

2.2 How forced alignment works

ProsodyLab, FAVE-Align, LaBB-CAT, BAS, and EasyAlign rely on the proprietary Hidden Markov toolkit (Young et al. Reference Young, Woodland and Byrne1993), and the Montreal Forced Aligner (MFA) relies on the open-source Kaldi (Povey et al. Reference Povey, Ghoshal, Boulianne, Burget, Glembek, Goel, Hannemann, Motlček, Qian, Schwarz, Silovský, Stemmer and Veselý2011), which is a type of neural network. Regardless of program, the inputs are always (1) an orthographic transcription, (2) a sound file, and (3) a pronunciation dictionary. The output is a phonetically-segmented file for use in Praat (Boersma & Weenink Reference Boersma and Weenink2017). The orthographic transcription is often a tab-delimited file outputted by ELAN (Sloetjes & Wittenburg Reference Sloetjes, Peter, Nicoletta, Khalid, Bente, Joseph, Jan, Stelios and Daniel2008) that has start and end times for each phrase/breath group (see Figure 1). The pronunciation dictionary is a text file that has pronunciation entries for every word in the language, which often can be as high as 30 or 40 possibilities for long compound words. This can be seen in Figure 2 where cirkusartist ‘circus performer’ has a canonical pronunciation option like [²ˈsɪr.kɵs.₂aˌʈɪst] on line 21, but a series of elided options such as [²ˈsɪr.kʂ̩.₂ˌʈɪs] on line 14. ^{Footnote 4} The final output, exemplified in Figure 3, is a Textgrid file for use with Boersma & Weenink’s (Reference Boersma and Weenink2017) Praat that, as we discuss in the following sections, can vary in accuracy depending on the aligner at hand.

Figure 1.

INPUTS 1 and 2: Five-column tab-delimited transcription input for FAVE-Align, produced with ELAN, and sound file.

Figure 2.

INPUT 3: Pronunciation dictionary with all possible pronunciations using ASCII characters for IPA.

Figure 3.

OUTPUT: Phonetically segmented file that is readable in Praat.

Most of the programs are free of cost (with the exception of the GlobalPhone extension of the MFA), and they provide various amounts of source code to the public along with varying degrees of written instructions for customizing the software to new languages. FAVE-Align stands out because it was specifically designed for sociolinguistic purposes and because it has shown the highest accuracy rates for the alignment of spontaneous vernacular speech (Yuan et al. Reference Yuan, Ryant, Liberman, Stolcke, Mitra and Wang2013). ProsodyLab and the MFA stand out because they provide the most robust assistance for training new languages. Additionally, the MFA is wrapped, which means it can be used out of the box with no subsidiary installations (e.g. Python).

FAVE-Align and the MFA are also noteworthy because they can process large sound files. They break files into chunks, align them, and concatenate them back together – all behind the scenes. This is very useful for any large-scale sociolinguistic project, but obviously less important for small projects. The remaining other programs require the user to manually break sound and transcription files down into one file per breath group. MFA is additionally noteworthy because it is relatively user-friendly and has an out-of-the-box trainer for new languages (should one have sufficient transcribed material handy).

We have offered a review of the various aligners on the market because this paper is, after all, about forced alignment. We would like, however, to point out that in the case of the Nordic languages, the comparative merits of each aligner do not matter much. In the case of Swedish, we had neither sufficient material to train an aligner like the MFA, nor were there pre-trained models available (and even today, the MFA model for Swedish sits behind a paywall). The picture is the same for Danish, Estonian Swedish, Faroese, Fenno-Swedish, Icelandic, the Northern and Western Norwegian dialects, and Övdalian. Absent of a large corpus of transcribed material, researchers will not be able to use MFA’s out-of-the-box trainer. The only reasonable alternative is the one we propose here.

2.3 Teasing apart the benefits of forced alignment

The purpose of this paper is to share a resource – a FAVE ‘hack’, if you will – to help phoneticians save time. Therefore, we will first devote this section to unpacking where exactly the most time is spent in the segmentation process. In doing so, we hope to demonstrate convincingly that there is a limit to the additional amount of time one can save after a certain accuracy threshold.

There is indeed a consistent positive relationship between alignment accuracy and time saved – if one wishes to extract data from uncorrected files, which is often the practice for variationist projects that take formant measurements from the nucleus of, for example, 25,000-plus vowels within a corpus (Dodsworth & Benton Reference Dodsworth and Benton2017:377). However, for analyses of rhythm (Torgersen & Szakay Reference Torgersen and Anita2012; Thomas & Carter Reference Thomas and Carter2006; Young Reference Young2019), manual corrections are obligatory. Laboratory Phonetics studies, typically using smaller datasets, also mandate manually-aligned datasets (Chodroff & Wilson Reference Chodroff and Wilson2017:33; Cole et al. Reference Cole, Hualde, Smith, Eager, Mahrt and Napoleão de Souza2019:120). In such instances, the time needed to manually move an incorrect boundary is roughly the same for 5 ms off-mark as it is for 40 ms off-mark. What saves time is fewer inaccurate boundary placements, with the degree of accuracy being more or less unimportant once the boundary error crosses a pre-established threshold.

Importantly, those time savings are marginal when compared to the time needed to manually build boundaries and populate the resulting cells with the appropriate phonetic orthography. To illustrate what we mean, take the following example. The recording that contains the first breath group from Figure 1 (Cirkusen var på väg! Deras plakat) lasts 2.37 seconds. ^{Footnote 5} We set a timer while the first author conducted the following tasks in Praat:

1. 1. Building tiers, then boundaries between words; populating the resulting cells: 2m 26s

2. 2. Building boundaries between phonemes; populating the resulting cells: 4m 24s

3. 3. Proofing boundaries; making final edits: 2m 19s

It takes 9 minutes 9 seconds (549 seconds) to manually align a 2.37-second transcription, which makes our segmentation-to-recording ratio 232:1. ^{Footnote 6} Observe, however, that more than 75% of that time is spent building the boundary architecture and populating cells. Any program that can automatically do that has the potential to save a lot of time, regardless of how accurate boundary placement is. A program that can accurately place the boundaries is also a boon, but that is in many respects a secondary benefit.

It is this fact that motivated our choice to build a prototype from FAVE-Align rather than training an entirely new model for Swedish. Since we had no guarantee for future alignment accuracy, we felt that the rapid adaptation of a pre-existing aligner was the more prudent investment to make, since it would eliminate steps 1 and 2 no matter what. This is also the viewpoint taken by the researchers who paved the way for this study and used untrained aligners for typologically-rare endangered languages (DiCanio et al. Reference DiCanio, Nam, Whalen, Bunnell, Amith and García2013; Coto-Solano & Solórzano Reference Coto-Solano and Sofía2017; Coto-Solano et al. Reference Coto-Solano, Nicholas and Wray2018; Strunk et al. Reference Strunk, Schiel, Seifart, Nicoletta, Khalid, Thierry, Hrafn, Bente, Joseph, Asuncion, Jan and Stelios2014). Although the accuracy levels were poor, they nonetheless saved the authors considerable time in their alignment endeavors.

2.4 Identifying acceptable accuracy benchmarks

If one accepts the review presented in the preceding section, then nearly any level of accuracy is acceptable as a starting point from which to manually correct boundaries. Of course, the literature on forced alignment is not as permissive. It has established a consistent range of performance metrics that are reviewed below. We will later apply these same metrics as a way to assess the quality of our Swedish-language adaptation.

Many metrics circulate, and this can often make cross-comparability within the literature challenging. This paper will therefore limit itself to the four most common metrics: (1) median onset difference from manual alignments, (2) mean onset difference from manual alignments, (3) the percentage of boundaries that fall within 10 ms of the manual alignment, and (4) the percentage of boundaries that fall within 20 ms of the manual alignment.

As regards the median and mean differences, some studies have calculated these solely for vowels (Evanini Reference Evanini2009:56) or have calculated them from log-transformed absolute values (Wilbanks Reference Wilbanks2015; Gorman et al. Reference Gorman, Howell and Wagner2011). Here, we calculate them for all phonemes. Some studies have also used standard deviations (Labov et al. Reference Labov, Rosenfelder and Fruehwald2013) or the percentage of boundaries that fall within 5, 25, 30, 40, 50, and 100 milliseconds of the manual benchmark (Cosi et al. Reference Cosi, Falavigna and Omologo1991:695; McAuliffe et al. Reference McAuliffe, Socolof, Mihuc, Wagner and Sonderegger2017:500). We chose not to include these metrics because their adoption is not sufficiently widespread. The below review will first cover automatic alignment benchmarks followed by manual alignment benchmarks. In select cases where visual figures are provided with no actual number, we have estimated the number by lining a straight edge between the plot and the axes (Evanini Reference Evanini2009; Yuan & Liberman Reference Yuan and Mark2008; Cosi et al. Reference Cosi, Falavigna and Omologo1991). While different papers have rounded to different decimal levels, we round to the nearest whole percentage or millisecond.

2.4.1 Benchmarks for automatic alignment

Table 1 contains a schedule of the benchmarks laid out in the literature for forced alignment that we will discuss in the ensuing prose.

Table 1.

Schedule of the benchmarks set in the literature according to the four most popular measurements. (Abbreviations: AE American English; BE British English; S spontaneous speech; R read-aloud speech; ms milliseconds; pct percentage.)

Cosi et al. (Reference Cosi, Falavigna and Omologo1991) is the earliest paper on phonetic forced alignment that we are aware of. They built an aligner for spontaneous Italian speech that had a mean error of 27 ms when compared to manually-aligned boundaries. For the 10-millisecond and 20-millisecond benchmarks, they were able to achieve circa 41% for the former and between 57% and 64% for the latter (Cosi et al. Reference Cosi, Falavigna and Omologo1991:695).

Yuan & Liberman (Reference Yuan and Mark2008), the most commonly-cited study for FAVE-Align, reported that approximately 70% of the boundaries fell within 10 ms of the manual standard and that approximately 80% of the boundaries fell within 20 ms of the manual standard (Yuan & Liberman Reference Yuan and Mark2008:4). These measurements were calculated on the original US Supreme Court Justice corpus upon which FAVE-Align was also modeled. In later work, Yuan et al. (Reference Yuan, Ryant, Liberman, Stolcke, Mitra and Wang2013:2308) proposed explicit phone boundary models within the Hidden Markov Model framework that improved the accuracy to 78% and 94% for the 10- and 20-millisecond error ranges, respectively.

Gorman et al. (Reference Gorman, Howell and Wagner2011) compared the performance of FAVE-Align with their newly-developed ProsodyLab Aligner on spontaneous American English taken from a television media corpus. They found FAVE-Align to have a median boundary error of 12 ms and a mean boundary error of 21 ms. For ProsodyLab, this was 12 ms and 31 ms, respectively. Ten- and 20-millisecond benchmarks were not calculated.

McAuliffe et al. (Reference McAuliffe, Socolof, Mihuc, Wagner and Sonderegger2017) assessed FAVE-Align and their newly-proposed Montreal Forced Aligner on read-aloud and spontaneous American English. For spontaneous speech run through FAVE-Align, the mean error was 19 ms, and the median error was 12 ms (McAuliffe et al. Reference McAuliffe, Socolof, Mihuc, Wagner and Sonderegger2017:501). For read-aloud speech run through FAVE-Align, the mean error was 22 ms, and the median error was 13 ms. Boundary-threshold percentages were not reported for FAVE-Align; they were, however, reported for the Montreal Forced Aligner. These were 41% within 10 ms for spontaneous speech and 36% within 10 ms for read-aloud speech (McAuliffe et al. Reference McAuliffe, Socolof, Mihuc, Wagner and Sonderegger2017:500). Twenty-millisecond thresholds were not calculated. What is particularly interesting about these results is that read-aloud speech aligned less accurately than spontaneous speech.

MacKenzie & Turton (Reference MacKenzie and Danielle2020) later tested FAVE-Align on read-aloud and spontaneous British English and found 83% of read-aloud phones to fall within 20 ms of the manual benchmark with a mean displacement of 8 ms. They found between 76% and 90% of spontaneous boundaries to fall within 20 ms of the manual benchmark with a mean displacement ranging between 8 ms and 20 ms (MacKenzie & Turton Reference MacKenzie and Danielle2020:9). Neither median errors nor 10-millisecond performance metrics were calculated.

Goldman (Reference Goldman2011), in his development of EasyAlign for Praat, tested its accuracy on two fifteen-minute excerpts of spontaneous English and French speech. He compared performance against the alignments of two manual transcribers. For English, 50% and 51% of automatic alignments fell within 10 ms of the standards set by human aligners 1 and 2, respectively; 77% and 75% fell within 20 ms. For French, 49% and 52% of automatic alignments fell within 10 ms of the standards set by human aligners 1 and 2, respectively; 79% and 82% fell within 20 ms.

Wilbanks (Reference Wilbanks2015) built a forced aligner for Spanish (FASE) that attained a 45% agreement rate for the 10-millisecond range and 70% for the 20-millisecond range. The mean difference between FASE and human alignment was 21 ms.

Lastly, Hosom (Reference Hosom2009) developed his own aligner for read-aloud English that is the sole aligner to come close to the standards set by FAVE-Align; namely, 80within 10 ms and 93% within 20 ms of his manual alignments (Hosom Reference Hosom2009:364). Important, however, is that the standards set by FAVE-Align are based on sponta- neous speech whereas Hosom’s (Reference Hosom2009) metrics are from read-aloud speech within the TIMIT corpus.

The trend between read-aloud speech and spontaneous speech is not at all as consistent as one would have thought; in other words, the popular aligners have not always fared better on read-aloud speech. Therefore, we have decided to consolidate both speech registers for the ensuing synopsis on benchmarks: The lower bounds in the literature on automatic alignment for (1) median onset difference from manual alignments, (2) mean onset difference from manual alignments, (3) the percentage of boundaries that fall within 10 ms of the manual alignment, and (4) the percentage of boundaries that fall within 20 ms of the manual alignment are 13 ms, 31 ms, 36%, and 57%, respectively. The upper bounds in the literature are 12 ms, 8 ms, 80%, and 94%, respectively.

3.1 Adapting FAVE-Align to Swedish

FAVE-Align has transparent architecture and ample documentation, which makes it particularly handy for adaptation. Although is has been adapted before DiCanio et al. Reference DiCanio, Nam, Whalen, Bunnell, Amith and García2013; Coto-Solano & Solórzano Reference Coto-Solano and Sofía2017; Coto-Solano et al. Reference Coto-Solano, Nicholas and Wray2018), detailed instructions for doing so have never been shared, which has resulted in an unfortunate stream of duplicated and uncoordinated efforts. The first author, who has expertise in Swedish phonetics, therefore scoured the code and identified change spots that would allow the use of the English HTK models for the closest corresponding Swedish phoneme. The second author, a seasoned programmer, proofed these change spots and made the hardcoded changes more pythonic. The original English monophones in FAVE-Align are done in ARPAbet, which is an ASCII-compatible system created by the Advanced Research Projects Agency’s (ARPA) Speech Understanding Project. We created a similar system for Swedish that we refer to here as SweFAbet.

Table 2 provides a list of the Swedish phoneme inventory. The first column contains the SweFAbet monophone, the second column the corresponding IPA symbol, the third column the most common corresponding grapheme, the fourth column a Swedish lexical example (some are loanwords; e.g. cok), the fifth column the closest English phoneme, and the sixth column the ARPAbet monophone for that closest English phoneme.

Table 2.

SweFAbet, corresponding IPA, grapheme, Swedish lexical example,^{Footnote 8} and closest English phoneme with ARPAbet

The closest phoneme match was subjectively determined by the first author, and no testing was conducted to assess which phoneme would be more suitable. For example, Central Swedish nät falls between American English trap and dress, so we decided arbitrarily on dress (Arpabet EH). For Central Swedish /ɧ/, there are strong arguments for both selecting American /h/ and /ʃ/, so we decided arbitrarily on /h/. It is because of this process that we have referred to our adaptation as ‘crude’ and ‘rapid’. Testing and optimizing phoneme matches would contradict the original aim of rapid prototype adaptation.

These adaptations are made in just six different locations within the FAVE-Align code. Since one aim of this paper is to be a resource for other researchers who wish to build a similar rapid prototype, detailed instructions on how we did this are provided in the Appendix.

After we programmed these substitutions in, we subsequently also built a prototype for Danish (not discussed in this paper), and the second author built a Unicode-8 converter, an IPA converter, and a language-general shell to host the Danish, English, and Swedish aligners within one program (LG-FAVE; Young & McGarrah Reference Young and Michael2017). The program is free and accessible at https://github.com/mcgarrah/LG-FAVE.

3.2 Procuring and adapting a pronunciation dictionary

The larger project for which this Swedish adaptation was built required a comprehensive dictionary (Young Reference Young2019), and two resources were particularly suitable for adaptation to the FAVE-Align format: Folkets Lexikon and the NTS pronunciation dictionary. These would not have been necessary for an experimental project that used, for example, a limited number of read-aloud sentences (e.g. Chodroff & Wilson Reference Chodroff and Wilson2017). Nonetheless, we have decided to dedicate some space here to the procurement of our dictionary because, as argued in Section 2.3, 75% of the time saved in automatic transcription comes from having a pronunciation dictionary that is both comprehensive and accurate. Furthermore, the NTS pronunciation dictionary is also publicly available for Danish and Eastern Norwegian (‘bokmål’), so our adaptation can serve as a guideline for those who wish to duplicate SweFA for these varieties.

3.3 Testing SweFA’s performance

SweFA was tested on the speech of nine adult male speakers of Stockholm Swedish. Three of them speak the received Stockholmian variety, which is closest to what Riad (Reference Riad2014) refers to as Central Swedish (centralsvenska); three speak the traditional working-class variety, sometimes referred to as Södersnack or Ekensnack (Kotsinas Reference Kotsinas and Gertrud1988b); three speak Stockholm’s multiethnolect, sometimes referred to as Rinkeby Swedish or Suburban Swedish (förortssvenska) (Kotsinas Reference Kotsinas, Per, Viveka, Torbjörn and Pettersson1988a; Young Reference Young2018).

The geographic origin of the speakers is plotted on the map in Figure 4. The map includes Stockholm’s metro system because this is the spatial framework to which the city’s residents typically associate its social dialects (Bijvoet & Fraurud Reference Bijvoet and Fraurud2012). One might hear the comment ‘he sounds very blue line’ as a reference to Stockholm’s multiethnolect. Likewise, one might hear ‘that’s so green line’ in reference to the habitus of the white working class. In this study, speakers of the received variety hail from the City Center and affluent suburbs. Speakers of working-class Stockholmian hail from the traditional white working-class strongholds in the Southeast. Speakers of Stockholm’s multiethnolect hail from the multiethnic suburbs in the Northwest and Southwest.

Figure 4.

Map of greater Stockholm and its metro. Home neighborhoods of the nine speakers are plotted, and speakers are itemized according to their respective social dialects.

Two speech styles were recorded for each of the nine speakers: spontaneous and reading. Both styles were taken from sociolinguistic interviews conducted by the first author. Criteria for treatment as ‘spontaneous’ were the presence of swearing, channel cues (Labov Reference Labov1972:113) and/or a topic that was engaging for the speaker such as danger of death or supernatural occurrences (Labov Reference Labov1972). The reading task occurred at the end of the interview whereby the participant was asked to read an adaptation of Cirkusen, a speech-pathology diagnostic passage that contains multiple exemplars of every Swedish phoneme and tone accent (Morris Zetterman Reference Morris and Helena2011).

Recordings were made on individual Zoom H1 recorders with self-powered Audio-Technica lavalier microphones in a quiet setting with minimal background noise. They were recorded in wav format, mono, with a sample rate of 16,000 Hz. The speech material was orthographically transcribed by native-language transcribers, financed by a grant from the Sven och Dagmar Salén Foundation. The transcriptions were then checked by the first author and subsequently phonetically time-aligned using SweFA. The first author then manually corrected the segmentations in accordance with standard segmentation protocol and the guidelines provided in Engstrand et al. (Reference Engstrand, Bruce, Elert, Eriksson and Strangert2000). Manual correction of the alignments took an average of 68 seconds per recorded second (something that we discuss in Section 4). Segmental metrics were extracted using a customized adaptation of Brato’s (Reference Brato2015) script for Praat (Boersma & Weenink Reference Boersma and Weenink2017).

For the spontaneous samples, pauses and hesitation markers were removed, and the first 1000 boundaries were measured. For the reading samples, the entire recording was measured after pauses and hesitation markers were removed, resulting in a range between 954 and 1040 boundaries.