Audio Contains Exclusive Information

Tens of thousands of studies have decisively established the importance of nontextual cues in human speech.Footnote ¹ Among other well-replicated results, they have shown that respondents’ perceptions—including dehumanization of those with opposing political views (Schroeder and Epley Reference Schroeder and Epley2016)—differ dramatically when exposed to audio recordings of speech, as opposed to transcripts alone. Countless experiments in linguistics and psychology have explored specific auditory mechanisms through which a speaker’s voice shapes the information gleaned by listeners on numerous dimensions. Speech audio has been shown to convey information about speakers’ static type (e.g., power), as well as their time-varying characteristics (e.g., emotional state). We briefly review this literature.

Political Science Already Studies Audio

Audio recordings of speech are thus indisputably a rich source of data. But how often are these recordings available in contexts of interest to political scientists? We now demonstrate that the answer is “quite often.” In fact, a wide range of research has already studied audio recordings of political speech—but in virtually every domain, researchers have done so by extracting transcripts, then discarding the remainder of their data.

In “A New Quantity of Interest in Judicial Behavior,” we consider one such domain. Here, published research has focused almost exclusively on the text of oral arguments (Black, Sorenson, and Johnson Reference Black, Sorenson and Johnson2013; Kaufman, Kraft, and Sen Reference Kaufman, Kraft and Sen2018; Ringsmuth, Bryan, and Johnson Reference Ringsmuth, Bryan and Johnson2013).Footnote ³ For example, Black et al. (Reference Black, Treul, Johnson and Goldman2011) examine how justices question parties in oral arguments, showing that text-based measures of affective questioning can signal voting decisions. In a direct comparison, we demonstrate that a comparable audio-based measure outperforms this prior work by three times, using its own evaluation task.

However, the Supreme Court is hardly the only context in which political scientists are already studying speech. Countless other studies have examined political debates (Bayley Reference Bayley2004; Benoit Reference Benoit2013; Conway et al. Reference Conway, Gornick, Burfeind, Mandella, Kuenzli, Houck and Fullerton2012; Fridkin et al. Reference Fridkin, Kenney, Gershon, Shafer and Woodall2007; Hart and Jarvis Reference Hart and Jarvis1997; Thomas, Pang, and Lee Reference Thomas, Pang and Lee2006), campaign advertisements (Carlson and Montgomery Reference Carlson and Montgomery2017; Fridkin and Kenney Reference Fridkin and Kenney2011; Spiliotes and Vavreck Reference Spiliotes and Vavreck2002), campaign speech (Bligh et al. Reference Bligh, Merolla, Schroedel and Gonzalez2010; Degani Reference Degani2015; Laver, Benoit, and Garry Reference Laver, Benoit and Garry2003; Olson et al. Reference Olson, Yu, Poe, Trantham and Waterman2012; Schroedel et al. Reference Schroedel, Bligh, Merolla and Gonzalez2013), legislative speech (Herzog and Benoit Reference Herzog and Benoit2015; Lauderdale and Herzog Reference Lauderdale and Herzog2016; Proksch and Slapin Reference Proksch and Slapin2012; Reference Proksch and Slapin2015; Quinn et al. Reference Quinn, Monroe, Colaresi, Crespin and Radev2010; Schwarz, Traber, and Benoit Reference Schwarz, Traber and Benoit2017; Slapin and Proksch Reference Slapin and Proksch2008), television news (Behr and Iyengar Reference Behr and Iyengar1985; Mermin Reference Mermin1997; Oegema and Kleinnijenhuis Reference Oegema and Kleinnijenhuis2000; Rozenas and Stukal Reference Rozenas and Stukal2019; Sanders and Gavin Reference Sanders and Gavin2004; Semetko and Valkenburg Reference Semetko and Valkenburg2000; Young and Soroka Reference Young and Soroka2012), talk radio (Conroy-Krutz and Moehler Reference Conroy-Krutz and Moehler2015; Hofstetter et al. Reference Hofstetter, Barker, Smith, Zari and Ingrassia1999; Ross Reference Ross2016; Sobieraj and Berry Reference Sobieraj and Berry2011), and political addresses (Cohen Reference Cohen1995; Ritter and Howell Reference Ritter and Howell2001; Rule, Cointet, and Bearman Reference Rule, Cointet and Bearman2015; Young and Perkins Reference Young and Perkins2005).

Each of these studies used text analysis to study a political context in which communication was not written and read as text, but rather was spoken and heard as audio. Given the relative youth of text analysis methods, it is perhaps surprising how often recorded speech is analyzed in this way. The mismatch between data and methods results in the inevitable loss of nontextual information, suggesting that richer models have the potential to contribute to research in each of these domains.

Advances over Existing Approaches

Outside of political science, a large and interdisciplinary body of research has sought to model or classify human speech. A common approach is to collapse each utterance into a vector of descriptive statistics (e.g., mean and standard deviation of pitch), which can be used in standard machine-learning classifiers (Dellaert, Polzin, and Waibel Reference Dellaert, Polzin and Waibel1996; McGilloway et al. Reference McGilloway, Cowie, Douglas-Cowie, Gielen, Westerdijk and Stroeve2000). However, these reduced representations discard enormous amounts of auditory data. To avoid this loss of information, hidden Markov models are often used to model time-series speech. The discrete latent states in this model map well to actual human speech, which is often represented in terms of discrete phonemes, or minimal units of sound.Footnote ⁷

The model of audio and speech structure (MASS) builds on these existing approaches in statistics and computer science in two main ways. First, computational constraints mean that typical analyses based on hidden Markov models (HMMs) are able to incorporate only a fraction of the features we incorporate. Nogueiras et al. (Reference Nogueiras, Moreno, Bonafonte and Mariño2001), for example, use just two features to represent a moment in time, while Kwon et al. (Reference Kwon, Chan, Hao and Lee2003) use 13 features and Mower et al. (Reference Mower, Metallinou, Lee, Kazemzadeh, Busso, Lee and Narayanan2009) use only the MFCC coefficients. Second, and more importantly, MASS is the first to directly model the flow of speech—that is, contextual and temporal patterns in vocal tone—in terms of the meaningful structural features encoded in conversation metadata.

The Substantive Importance of Speech Flow

Modeling the flow of speech permits principled empirical inquiry into a large and varied set of substantive questions about interaction between speakers. This enables the study of new questions and quantities of interest. For example, by modeling the dynamics of speech (i.e., change of flow in response to prior events), MASS allows researchers to ask and answer questions about how a line of argumentation affects downstream discourse. While these sorts of questions are common in theoretical models of sequential games (Dancey and Goren Reference Dancey and Goren2010; Ramirez Reference Ramirez2009), they are rarely tested empirically. We suggest that this is due to the absence of a suitable empirical model, and “Testing Theories of Supreme Court Deliberation” demonstrates how MASS can answer questions of this sort.

The Model

Suppose we have a conversation with $ U $ sequential utterances, each of which arises from one of $ M $ modes of speech. (To keep the exposition clear, here we consider the simplified setting in which a single conversation is analyzed. Online Appendix Section 1 presents the general multiconversation model, which is essentially identical.) Let $ {S}_u\in \left\{1,\dots, M\right\} $ denote the speech mode, or tone, of utterance $ u\in \left\{1,\dots, U\right\} $ . Broadly, the model contains two levels. The “upper” level, defined in Equations 1–2, characterizes the flow of speech, or the choice of $ {S}_u $ . We assume that the conversation unfolds as a time-varying stochastic process in which $ {S}_u $ is chosen based on the conversational context at that moment, encoded in the vector $ {\boldsymbol{W}}_u $ . In the “lower” level, we then outline a generative model for utterance $ u $ ’s auditory characteristics, $ {\boldsymbol{X}}_u $ . Importantly, this generative model will differ depending on the tone of voice selected in the upper model, $ {S}_u $ . Equations 3–4 present the lower level more formally. The model is summarized graphically in Figure 2, and we refer readers there for a holistic view of how the various model components fit together.

Figure 2.

Illustration of Generative Model

Note: The directed acyclic graph represents the relationships encoded in Equations 1–4. In utterance $ u $ , the speaker selects tone $ {S}_u $ based on “static” (i.e., externally given) time-varying covariates $ {\boldsymbol{W}}_u^{\mathrm{stat}.} $ as well as “dynamic” conversational history covariates $ {\boldsymbol{W}}_u^{\mathrm{dyn}.} $ . (In the illustration, $ {\boldsymbol{W}}_u^{\mathrm{dyn}.} $ depends only on the prior mode of speech, but more complex dynamic covariates can be constructed.) Based on the selected tone, the speaker composes an utterance by cycling through a sequence of sounds in successive moments, $ {R}_{u,1},{R}_{u,2},\dots $ , to form the word “mass.” Each sound generates the audio perceived by a listener according to its unique profile; $ {\boldsymbol{X}}_{u,t} $ is extracted from this audio.

We begin by modeling speech mode probabilities in each utterance as a multinomial logistic function of conversational context, $ {\boldsymbol{W}}_u $ . Note that $ {\boldsymbol{W}}_u $ may include functions of conversational history, such as aggregate anger expressed by previous speakers over the course of an argument—that is, $ {\sum}_{u^{\prime }<u}1\left({\boldsymbol{S}}_{u^{\prime }}=\mathrm{anger}\right) $ —which might induce a sharp retort in utterance $ u $ . These follow

(1) $$ {\Delta}_{u,m}=\exp \left({\boldsymbol{W}}_u^{\mathrm{\top}}{\zeta}_m\right)/\sum \limits_{m^{\prime }=1}^M\exp \left({\boldsymbol{W}}_u^{\mathrm{\top}}{\zeta}_{m^{\prime }}\right)\hskip1em \mathrm{and} $$

(2) $$ {S}_u\sim \mathrm{Cat}\left({\boldsymbol{\Delta}}_u\right), $$

where $ {\boldsymbol{\Delta}}_u=\left[{\Delta}_{u,1},\dots, {\Delta}_{u,M}\right] $ and $ {\zeta}_m $ is a mode-specific coefficient vector through which $ {\boldsymbol{W}}_u $ affects the relative prevalence of mode $ m $ . The tone of utterance $ u $ , $ {S}_u $ , is one of the primary quantities of interest, along with the coefficients $ {\zeta}_m $ that explain why certain tones are used more in particular contexts. However, generally speaking, tone is not directly observable to the analyst; the utterance’s auditory characteristics, $ {\boldsymbol{X}}_u $ , are the only available information. (As we discuss in the following section, the analyst will begin estimation by obtaining a sample of utterances with human-labeled tone.)

Each $ {\boldsymbol{X}}_u $ is a matrix describing the auditory characteristics of utterance $ u $ . In this matrix, the $ t $ -th row describes the audio at moment $ t $ in terms of $ D $ auditory features. Thus, the utterance audio is represented by a $ {T}_u\times D $ feature matrix, where $ {T}_u $ is the length of the utterance; because utterances may be of differing lengths, $ {\boldsymbol{X}}_u $ and $ {\boldsymbol{X}}_{u^{\prime }} $ may have differing numbers of rows.

To model the audio, we then assume that the $ m $ -th mode of speech is associated with its own Gaussian HMM that produces the auditory features as follows. At moment $ t $ in utterance $ u $ , the speaker enunciates the sound $ {R}_{u,t} $ —that is, the latent state, which may represent phonemes or phoneme groups such as plosives or fricatives. In successive moments, the speaker alternates through these latent sounds according to

(3) $$ \left({R}_{u,t}|{S}_u=m\right)\sim \mathrm{Cat}\left({\Gamma}_{R_{u,t-1},\ast}^m\right), $$

with $ {\Gamma}_{k,\ast}^m $ denoting rows of the transition matrix, $ \left[\Pr \left({R}_{u,t}=1|{R}_{u,t-1}=k\right),\dots, \Pr \Big({R}_{u,t}=K|{R}_{u,t-1}=k\Big)\right] $ . By modeling the usage patterns of different sounds in this way, we approximately capture the temporal structure that plays an important role in speech. (For example, most latent sounds are sustained for at least a few moments, and certain phonemes typically occur before the silence at the end of a word.) In turn, latent sound $ k $ is associated with its own auditory profile, which we operationalize as a multivariate Gaussian distribution with parameters $ {\boldsymbol{\mu}}^{m,k} $ and $ {\Sigma}^{m,k} $ . Finally, the raw audio heard at moment $ t $ of utterance $ u $ —the signal perceived by a listener—is drawn as

(4) $$ {\boldsymbol{X}}_{u,t}\sim \mathcal{N}\left({\boldsymbol{\mu}}^{S_u,{R}_{u,t}},{\Sigma}^{S_u,{R}_{u,t}}\right), $$

which completes the model. Thus, each mode of speech is represented with a rich and flexible HMM that nevertheless reflects much of the known structure of human speech. It is the differences in usage patterns and sound profiles—the Gaussian HMM parameters—that enable human listeners to distinguish one tone or speaker from another.

Estimation

We describe a procedure for estimating the model defined above, incorporating elements of both unsupervised and supervised learning. The researcher begins by determining the speech modes of interest, then identifying and labeling example utterances from each class. Within this training set—which might not be a subset of the primary corpus of interest—we consider each mode of speech in turn, using a fully unsupervised approach to learn the auditory profile and cadence of that speech mode. The results are applied to the full corpus to obtain “naïve” estimates of each utterance’s tone, based only on the audio features and ignoring conversational context. We then fit a model for the flow of conversation, use this to refine the “contextualized” tone estimates, and repeat in an iterative procedure. The specifics of each step are discussed below and in Online Appendix Section 1, and the workflow is outlined more formally in Algorithm 1.

Table 1 summarizes the data available for the primary corpus and training set, respectively indicated with $ \mathcal{C} $ and $ \mathcal{T} $ . The audio characteristics of each utterance, $ \boldsymbol{X} $ , are observed for both the primary corpus and the training set. However, human-labeled tone of speech, $ \boldsymbol{S} $ , is only known for the training set. We divide the conversational context into externally given but potentially time-varying “static metadata,” $ {\boldsymbol{W}}^{\mathrm{stat}.} $ , and deterministic functions of conversational history that dynamically capture the prior tones of speech, $ {\boldsymbol{W}}^{\mathrm{dyn}.} $ . The former is known for the primary corpus but may be unavailable for the training set, depending on how it is constructed; the latter is not directly observed for either.

Table 1.

Observed and Unobserved Quantities

Note: Data that are (un)available to the analyst are (un)boxed. Attributes of the primary corpus (training set) are indicated with $ \mathcal{C}(\mathcal{T}) $ superscripts. Raw audio features, $ \boldsymbol{X} $ , are observed for all utterances. The portion of the conversational context that relates to static metadata ( $ {\boldsymbol{W}}^{\mathrm{stat}.} $ ) is available for at least the primary corpus, but dynamic contextual variables that depend on the tone of prior utterances ( $ {\boldsymbol{W}}^{\mathrm{dyn}.} $ ) can only be estimated. In general, the tone of each utterance ( $ \boldsymbol{S} $ ) is also unobserved, but the analyst possesses a small training set with human-labeled utterances.

Our ultimate goal is to estimate the conversational flow parameters, $ \zeta $ , and the auditory parameters of each tone, which we gather in $ {\Theta}^m=\left({\mu}^m,{\Sigma}^m,{\Gamma}^m\right) $ for compactness. In what follows, we also refer to the collection of all tone parameters as $ \Theta ={\left({\Theta}^m\right)}_{m\in \left\{1,\dots, M\right\}} $ . Under the model described in Equations 1–2, the likelihood can be expressed as

(5) $$ \mathrm{\mathcal{L}}\left(\zeta, \Theta \hskip0.5em |\hskip0.5em {\boldsymbol{X}}^{\mathcal{T}},{\boldsymbol{S}}^{\mathcal{T}},{\boldsymbol{X}}^{\mathcal{C}},{\boldsymbol{W}}^{\mathrm{stat}.,\mathcal{C}}\right)\hskip0.5em =\hskip0.5em f\left({\boldsymbol{X}}^{\mathcal{C}}\hskip0.5em |\hskip0.5em \zeta, \Theta, {\boldsymbol{W}}^{\mathrm{stat}.,\mathcal{C}}\right)\hskip0.5em f\left({\boldsymbol{X}}^{\mathcal{T}}\hskip0.5em |\Theta, {\boldsymbol{S}}^{\mathcal{T}}\right), $$

with one factor depending only on the primary corpus and another containing only the training set.

As a concession to computational constraints, we estimate the parameters in a stagewise fashion. The auditory parameters, $ \Theta $ , are calculated by maximizing the partial likelihood, $ f\left({\boldsymbol{X}}^{\mathcal{T}}\hskip0.5em |\hskip0.5em \Theta, {\boldsymbol{S}}^{\mathcal{T}}\right) $ , corresponding to the training factor, rather than the full likelihood in Equation 5 (Wong Reference Wong1986). The full likelihood is then maximized with respect to the conversational flow parameters $ \zeta $ , conditional on $ \Theta $ . The factorization and a detailed discussion of stagewise estimation are presented in Online Appendix Section 1.1.

In Online Appendix Section 1.2, we detail our procedure for estimating the auditory profile and cadence for each speech mode. First, training utterances are divided according to their tone labels. Because the partial likelihood can be neatly factorized further as $ f\left({\mathbf{X}}^{\mathcal{T}}\hskip0.5em |\hskip0.5em \Theta, {\boldsymbol{S}}^{\mathcal{T}}\right)={\prod}_{m=1}^M{\prod}_{u\in \mathcal{T}}f{\left({\boldsymbol{X}}_u|{\Theta}^m\right)}^{\mathbf{1}\left({S}_u=m\right)} $ , $ {\hat{\Theta}}^m $ can be independently estimated for each speech mode with no further loss of information. For all training utterances of speech mode $ m $ , a regularized variant of the Baum–Welch algorithm, a standard estimation procedure for hidden Markov models, is used to obtain $ {\hat{\Theta}}^m $ for the corresponding mode. Each of the resulting $ M $ tone-specific models are then applied to each utterance $ u $ in the primary corpus to obtain the corrected emission probabilities $ f{\left({\boldsymbol{x}}_u|{\hat{\Theta}}^m,{S}_u=m\right)}^{\rho } $ , which represents the probability that the utterance’s audio was generated by speech mode $ m $ ; this captures the extent to which the audio “sounds like” the relevant training examples. Naïve tone estimates can then be computed by combining these with the overall prevalence of each tone via Bayes’ rule. The corrective factor, $ \rho $ , approximately accounts for unmodeled autocorrelation in the audio features and ensures that the naïve estimates are well calibrated (for details, see Online Appendix Section 1.3). This shared correction, along with the number of latent sounds and strength of regularization, are determined by likelihood-based cross validation (van der Laan, Dudoit, and Keles Reference van der Laan, Dudoit and Keles2004) in the training set.

We now briefly describe an expectation-maximization algorithm for the conversation-flow parameters, $ \zeta $ , reserving derivations and other details for Online Appendix Section 1.4.Footnote ⁸ An inspection of Equation 5 shows that this procedure will depend only on $ f\left({\boldsymbol{X}}^{\mathcal{C}}\hskip0.5em |\zeta, \Theta, {\boldsymbol{W}}^{\mathrm{stat}.,c}\right) $ , since the remaining term does not involve $ \zeta $ . We proceed by augmenting the observed data with the latent tones, $ {\boldsymbol{S}}^{\mathcal{C}} $ , and the conversation-history variables that depend on them, $ {\boldsymbol{W}}^{\mathrm{dyn}.,\mathcal{C}} $ . The augmented likelihood, $ f\left({\boldsymbol{X}}^{\mathcal{C}},{\boldsymbol{S}}^{\mathcal{C}},{\boldsymbol{W}}^{\mathrm{dyn}.,\mathcal{C}}\hskip0.5em |\hskip0.5em \zeta, \Theta, {\boldsymbol{W}}^{\mathrm{stat}.,\mathcal{C}}\right) $ , is then iteratively optimized. However, the closed-form expectation of the augmented likelihood is intractable. We therefore replace the full E-step with a blockwise procedure that sweeps through the unobserved E-step variables sequentially.Footnote ⁹ Finally, the maximization step for $ \zeta $ reduces to a weighted multinomial logistic regression in which $ \mathbf{1}\left({S}_u=m\right) $ is fit on $ \unicode{x1D53C}\left[{\boldsymbol{W}}_u|{S}_{u-1}={m}^{\prime}\right] $ for every possible $ m $ and $ {m}^{\prime } $ , with weights corresponding to the probability of that transition.

Finally, we observe that the unmodeled autocorrelation discussed above renders model-based inference invalid. To address this issue, we estimate the variance of parameter estimates by bootstrapping utterances in the training set, ensuring that dependence between successive moments in an utterance do not undermine our results (Online Appendix Section 1.5 discusses potential issues in bootstrapping). The full estimation procedure is outlined in Algorithm 1. Other quantities of interest, such as those discussed in “Testing Theories of Supreme Court Deliberation,” follow directly from the conversation-flow parameters, $ \zeta $ , or the auditory parameters, $ \Theta $ ; inference on these quantities follows a similar bootstrap approach.

Data: Audio features ( $ {\boldsymbol{X}}^{\mathcal{C}},{\boldsymbol{X}}^{\mathcal{T}} $ ), static metadata for primary corpus ( $ {\boldsymbol{W}}^{\mathrm{stat}.,\mathcal{C}} $ )

Result: Auditory parameters $ \Theta $ , conversational flow parameters $ \zeta $

Procedure:

1. Define problem.

Analyst determines tones of interest and rubric for human coding. Human-coded tone labels are obtained for training set ( $ {\boldsymbol{S}}^{\mathcal{T}} $ ).

2. Fit auditory parameters ( $ \Theta $ ) by maximizing partial likelihood on training set ( $ \mathcal{T} $ ).

3. Fit conversational flow parameters (ζ) using primary corpus ( $ \mathcal{C} $ ), conditional on $ \Theta $ .

Algorithm 1: Stagewise Estimation Procedure. After defining the tones of interest and obtaining a labeled training set, the analyst conducts cross validation to set ancillary parameters such as the number of assumed sounds in each mode of speech (not depicted). After fixing the ancillary parameters, the cadence and auditory characteristics of each speech mode are estimated from the training set by an iterative expectation-maximization procedure. These speech parameters are then fixed, and the relationship between conversational context and flow of speech is estimated from the primary corpus. In the multiple-conversation case, the utterance loop in step 3 is nested within an outer loop over conversations. Statistical inference is conducted by resampling $ \mathcal{T} $ and repeating steps 2–3 within the bootstrapped training set (not depicted) to obtain bootstrap-aggregated point estimates and bootstrap variance estimates for flow-of-speech parameters and other quantities of interest.

Audio Data from Supreme Court Oral Arguments

We limit our analysis to the natural court that begins with the appointment of Justice Kagan and concludes with the passing of Justice Scalia, so that the composition of the Court remains constant for the entirety of the period we analyze. The Oyez data contains an accompanying textual transcript, speaker names for each utterance, and timestamps for utterance start and stop times. In addition, we inferred the target side (i.e., petitioner or respondent) of each justice’s question based on the side of the most recently speaking lawyer. Additional case data were merged from the Supreme Court Database (Spaeth et al. Reference Spaeth, Lee, Ruger, Whittington, Segal and Martin2014).

Using Oyez timestamps, we segmented the full-argument audio into a series of single-speaker utterances.Footnote ¹¹ As an additional preprocessing step, we drop utterances spoken by lawyers (each of whom usually appears in only a handful of cases) and Clarence Thomas (who spoke only twice in our corpus), focusing on the behavior of the eight recurrent speakers. We also drop procedural statements, along with utterances shorter than 2.5 seconds.Footnote ¹² After trimming and dropping cases in which the ideological direction is unclear, the resulting audio corpus contains 266 arguments and 95 hours of audio, comprising nearly 42,000 utterances and over 27 million frames.

The Quantity of Interest: Judicial Skepticism

In this section and the next, we introduce and employ a new measure of substantive importance to the study of courts: judicial skepticism, an individual’s vocal expression of their judgment about the argument at hand. Judicial skepticism is an important signal of disagreement with or incredulity about assertions and legal arguments, especially in the context of oral arguments.

To identify judicial skepticism in speech, we first randomly selected a training set of 200 utterances per justice to hand-classify as “skeptical” or “neutral” speech, allowing our assessments to reflect not only the vocal tone but also the textual content of the utterance. Thus, we define 16 modes of speech—two tones for each of the eight speaking justices.Footnote ¹³ During classification, we dropped the handful of utterances (roughly 5%) in which crosstalk or other audio anomalies occurred or in rare instances where the speaker’s identity was incorrectly recorded. The model is then estimated following Algorithm 1.

A Case Study of Judicial Skepticism

To illustrate the use of skepticism during the flow of oral arguments, we conducted a case study of Alabama Legislative Black Caucus v. Alabama, a racial gerrymandering case heard by the Supreme Court in 2014 that considered the legality of Alabama’s 2012 redistricting efforts. The study is described in depth in Online Appendix Section 3; we briefly summarize it here and demonstrate the application of MASS to this case in Figure 3.

Figure 3.

An Illustrative Example

Note: Panel A contains excerpts from Alabama Legislative Black Caucus v. Alabama, where Justices Scalia, Kennedy, and Breyer utilize neutral and skeptical tones in questioning. Call-outs highlight successive utterance pairs in which the speaker shifted from one mode to another (B.3), and continued in the same tone of voice (B.1 and B.2). Panels C.1 and C.2 illustrate the use of loudness (text size) and pitch (contours) within utterances: in the neutral mode of speech (C.1), speech varies less in pitch and loudness when compared with skeptical speech (C.2). Based on these and other features, MASS learns to categorize sounds into vowels (dark squares), consonants (light), and pauses (white). Call-outs D.1 and D.2 respectively identify sequential moments in which a “neutral” vowel is sustained (transition from the dark blue sound back to itself, indicating repeat) and the dark red “skeptical” vowel transitions to the light red consonant. Panel E shows the differing auditory characteristics of the “skeptical” vowel and consonant, which are perceived by the listener.

As background, the case arose when the Republican-led legislature redrew electoral districts in the face of declining urban population. In doing so, the legislature sought to pack Black voters into a small number of already heavily Democratic districts. The liberal Alabama Legislative Black Caucus argued that this practice violated the Voting Rights Act (VRA), a position ultimately supported by the Court’s decision, whereas conservative defenders of the new map argued that Section 5 of the VRA in fact forced the legislature to draw Black-dominated districts.

Figure 3 depicts two exchanges in which justices spar over this legal claim about Section 5—that a state must hold or increase the numerical percentage of Black voters in a district to maintain minorities’ “ability to elect their preferred candidates.” In the first exchange, beginning with Justice Scalia’s question, “Well, I thought the Section 5 obligation …,” Justice Scalia advocates the conservative view when questioning the liberal advocate. Median Justice Kennedy, perhaps influenced by this line of questioning, pursues it further and transitions into skepticism on the technical point of whether the liberal reading of the VRA constitutes an indefensible “one-way ratchet” on minority percentages. Justice Breyer then comes to the defense of the liberal side, asking the friendly rhetorical question—ostensibly to the advocate—of whether Justice Kennedy’s question was addressed by precedent. Later in the oral argument, these roles reverse. The figure depicts a subsequent exchange in which Justice Kennedy initiates a line of questioning, Justice Breyer attacks by skeptically saying to the conservative advocate, “I don’t know what the defense is possibly going to be,” and Justice Scalia comes to the rescue with a “softball” question.

These exchanges illustrate the sort of political interactions modeled by MASS. Panel 3.A depicts how skeptical and neutral speech are deployed throughout the discussion, and Panels 3.B.1–3 each highlight a justice’s choice of tone (e.g., the decision to switch from neutrality to skepticism, which we model using covariates such as conversational history or the ideology of the side currently being questioned). Panels 3.C.1–2 examine two utterances in depth, showing a subset of the auditory features that MASS relies on to infer speech tone. Each tone is modeled as a sequence of discrete sounds, like “vowel” or “silence”; their usage is shown in Panels 3.D.1–2, and their auditory content is in Panel 3.E.

Validating the Model

We conduct extensive validation of our model-based measure of judicial skepticism, confirming that MASS does in fact successfully estimate the quantity of interest. Due to space constraints, we summarize these efforts here; results are described in detail in Online Appendix Section 4.

First, we demonstrate that MASS recovers a measure that has high face validity. Online Appendix 4.1 presents randomly sampled utterances from the highest and lowest deciles of model-predicted skepticism. Those characterized by the model as skeptical include gentle mockery and doubtful questions, whereas model-predicted neutral utterances are factual statements and straightforward legal analysis.

Second, we examine content validity in Online Appendices 4.2–4.3. Our model detects skepticism based on linguistically and physiologically meaningful auditory features. In a model of Justice Kennedy’s expressed skepticism, compared with neutral questioning, we find that his speech is louder, contains more pitch variation, and is characterized by higher vocal tension. We caution that MASS does not take textual signals of skepticism into account, an important limitation on content validity. (Joint models of audio and text remain an important direction for future work.) However, we demonstrate that in the case of judicial speech, there are extremely few textual signals that distinguish skepticism from typical questioning.

Third, in Online Appendix 4.4, we estimate a lower bound on the out-of-sample performance of MASS, using cross-validation results from the lower stage of the model, corresponding to Equations 3–4 and excluding Equations 1–2. We find that out-of-sample accuracy of the lower-level auditory classifier is 68%, versus the 52% that would be obtained by randomly permuting labels; numerous additional performance metrics are reported in the appendix.Footnote ¹⁴

Fourth, we compare the performance of MASS with (1) human coders and (2) text-based classifiers. For the human comparison, we recruited native English speakers on a crowdworking site and evaluated their ability to recover ground-truth labels in legal argumentation. We found that when combining responses by majority vote, nonexpert listeners were able to detect 70% of judicial skepticism, outperforming MASS by a small margin. The performance of individual coders was lower, at 65%, suggesting that with a relatively small amount of domain-specific data, our model performs approximately as well as humans with a lifetime of experience in parsing non-domain-specific speech. For the textual comparison, we applied an elastic net to utterance word counts. The resulting trained text models were entirely degenerate, predicting the more common label in virtually every case.

Finally, we probe the predictive validity of our measure with a comparison to Black et al. (Reference Black, Treul, Johnson and Goldman2011), described in the next section.

Comparison with an Existing Measure

We conduct yet another validity test by contrasting our model with the approach of Black et al. (Reference Black, Treul, Johnson and Goldman2011), who use a measure based on directed pleasant and unpleasant words—operationalized with the Dictionary of Affect in Language (DAL; Whissell Reference Whissell2009)—to predict justice voting. We replicate and contrast results with those from a comparable measure of directed skepticism.Footnote ¹⁵ Specifics are discussed in Online Appendix 4.5. We find that a one-standard-deviation increase in directed unpleasant (pleasant) words is associated with a 2.8-percentage-point decrease (no difference) in the probability that a justice votes against a side. In comparison, a one-standard-deviation increase in directed skepticism, as estimated from auditory characteristics, is associated with a 7.9-percentage-point decrease in vote probability—nearly three times as large. Moreover, Figure 4 shows that unlike text-based results, these patterns are robust to the inclusion of both justice and case fixed effects.

Figure 4.

Predicting Justice Votes with Directed Skepticism and Directed Affective Language

Note: Horizontal error bars represent point estimates and 95% confidence intervals from regressions of justice votes on directed pleasant words, directed unpleasant words, and our audio-based directed skepticism. Red circles correspond to a specification with no additional controls; blue triangles report results with speaker fixed effects only, black squares with speaker and case fixed effects.

Why is speech tone so much more predictive of voting patterns than the use of affective words? One reason may be that DAL uses a cross-domain measure of word pleasantness, ignoring the fact that words often take on different meanings in legal contexts. For example, the the 10 most common “unpleasant” words in our corpus include “argument” and “trial,” whereas the five most common “pleasant” words include “read” and “justice.” However, as we show in other text-audio comparisons, a more likely explanation is that word choice is a noisy, high-dimensional, and difficult-to-measure signal of expressed emotion, whereas auditory tone is relatively structured and consistent.

However, we note that MASS exploits only the auditory channel of speech. While we show in Online Appendix 4.4 that this provides a clearer signal of skepticism than text in general, there are nonetheless cases when expressions of disbelief are spoken flatly. In one example, Justice Ginsburg states matter-of-factly, “Well, that would be fine if the statute said what you claim it said.” The utterance is clearly skeptical, yet our model predicts that it is 89% likely to be neutral speech due to its deadpan delivery. This counterexample highlights limitations in the use of any communication channel in isolation, suggesting that joint models of text and tone are a necessary direction for future work.

Next, we demonstrate the model with an application to speech in Supreme Court oral arguments, then conclude.