# Twists, Humps, and Pebbles: The Uneven Landscape of End-to-end Speech
Recognition and Demographics

Giuseppe Attanasio♣, Beatrice Savoldi♠, Dennis Fucci♢♠, Dirk Hovy♣

♣ Bocconi University, Milan, Italy
♢ University of Trento, Trento, Italy
♠ Fondazione Bruno Kessler, Trento, Italy
<EMAIL_ADDRESS>
# Larger and Massively Multilingual Is Not Fairer: Demographic Disparity in
Transformer Speech-To-Text Models

Giuseppe Attanasio♣, Beatrice Savoldi♠, Dennis Fucci♢♠, Dirk Hovy♣

♣ Bocconi University, Milan, Italy
♢ University of Trento, Trento, Italy
♠ Fondazione Bruno Kessler, Trento, Italy
<EMAIL_ADDRESS>
# Large Multilingual Speech Models Exhibit Gender Performance Gap

Giuseppe Attanasio♣, Beatrice Savoldi♠, Dennis Fucci♢♠, Dirk Hovy♣

♣ Bocconi University, Milan, Italy
♢ University of Trento, Trento, Italy
♠ Fondazione Bruno Kessler, Trento, Italy
<EMAIL_ADDRESS>
# Multilingual Automatic Speech Recognition Models Exhibit
Gender Performance Gap

Giuseppe Attanasio♣, Beatrice Savoldi♠, Dennis Fucci♢♠, Dirk Hovy♣

♣ Bocconi University, Milan, Italy
♢ University of Trento, Trento, Italy
♠ Fondazione Bruno Kessler, Trento, Italy
<EMAIL_ADDRESS>
# Multilingual Speech-to-text Models Exhibit Gender Performance Gaps

Giuseppe Attanasio♣, Beatrice Savoldi♠, Dennis Fucci♢♠, Dirk Hovy♣

♣ Bocconi University, Milan, Italy
♢ University of Trento, Trento, Italy
♠ Fondazione Bruno Kessler, Trento, Italy
<EMAIL_ADDRESS>
# Multilingual Speech Models for Automatic Speech Recognition Exhibit Gender
Performance Gaps

Giuseppe Attanasio♣, Beatrice Savoldi♠, Dennis Fucci♢♠, Dirk Hovy♣

♣ Bocconi University, Milan, Italy
♢ University of Trento, Trento, Italy
♠ Fondazione Bruno Kessler, Trento, Italy
<EMAIL_ADDRESS>
###### Abstract

Current voice recognition approaches use multi-task, multilingual models for
speech tasks like Automatic Speech Recognition (ASR) to make them applicable
to many languages without substantial changes. However, broad language
coverage can still mask performance gaps within languages, for example, across
genders. We systematically evaluate multilingual ASR systems on gendered
performance gaps. Using two popular models on three datasets in 19 languages
across seven language families, we find clear gender disparities. However, the
advantaged group varies between languages. While there are no significant
differences across groups in phonetic variables (pitch, speaking rate, etc.),
probing the model’s internal states reveals a negative correlation between
probe performance and the gendered performance gap. I.e., the easier to
distinguish speaker gender in a language, the more the models favor female
speakers. Our results show that group disparities remain unsolved despite
great progress on multi-tasking and multilinguality. We provide first valuable
insights for evaluating gender gaps in multilingual ASR systems. We release
all code and artifacts at https://github.com/g8a9/multilingual-asr-gender-gap.

Multilingual Speech Models for Automatic Speech Recognition Exhibit Gender
Performance Gaps

Giuseppe Attanasio♣, Beatrice Savoldi♠, Dennis Fucci♢♠, Dirk Hovy♣ ♣ Bocconi
University, Milan, Italy ♢ University of Trento, Trento, Italy ♠ Fondazione
Bruno Kessler, Trento, Italy<EMAIL_ADDRESS>

##  1 Introduction

Recent advancements in Automatic Speech Recognition (ASR) have showcased
remarkable capabilities of “one-model-fits-all” multi-task, multilingual
solutions (Radford et al., 2022; Communication et al., 2023; Chu et al.,
2023). These neural networks can transcribe, translate, or generate speech in
an unprecedented number of languages. Consequently, these models have found
widespread adoption111Anecdotally, all major cloud providers and countless
smaller companies list a tutorial on how to deploy and serve Whisper (Radford
et al., 2022) on their platforms. and are being integrated into applications
for the general public.222https://techcrunch.com/2023/06/19/gladia-turns-any-
audio-into-text-in-near-real-time/

Figure 1: Model error rate gaps between female and male speakers. Average
across language families and data sets. Empty shapes show models that
advantage women, filled shapes advantage men. Pairs with one shape indicate
gap on one group only. Language count per family in brackets. Ideal scores on
the “0” line.

However, applicability to many languages should be one of many optimizations.
Voice and language production are among the strongest identity traits, widely
varying across people, including genders. These differences have physiological
and sociocultural roots (e.g., Labov, 1964; Wolfram, 2004; Alim, 2004; Zimman,
2017, 2020). While physiological differences are rooted in biological sex
(e.g., vocal tract length), sociocultural differences (e.g., prescribed
registers) reflect gender identity. In this paper, we account for both.

Because of their varied features and societal implications, gendered voice and
speech differences—usually as binary distinction—have been extensively studied
(Coleman, 1976; Busby and Plant, 1995; Hillenbrand and Clark, 2009). However,
modeling gender also poses various challenges for ASR, often leading to
performance disparities usually favoring male Tatman (2017); Garnerin et al.
(2019, 2021) but sometimes female speakers Adda-Decker and Lamel (2005); Feng
et al. (2021); Liu et al. (2022). Those performance disparities can lead to
unequal service quality, which is especially harmful to already socially
disadvantaged groups (Mengesha et al., 2021). However, most of those studies
are within one language (usually English). Evaluating the now widely deployed
multilingual ASR models frequently overlooks gendered differences, posing
fairness concerns.

We are the first to evaluate gendered performance gaps in massively
multilingual ASR models systematically. More specifically, we pose two
research questions. (Q1) Do multi-task, multilingual ASR models perform
equally well across speakers identifying as women, men, or neither of the two?
And, if so, (Q2) can we relate performance gasp to (socio)phonetic features or
how models encoded gender internally?

To answer Q1, we evaluate two state-of-the-art multilingual ASR models on
three datasets (read and spontaneous speech) covering 19 languages from seven
language families. Our results show that models do not perform equally well
across genders. Whether models favor male or female speakers depends on the
dataset and language. Figure 1 shows average error rates across the tested
language families. The model performance of speakers who do not identify with
either gender is lower than that of those identifying as male. We conduct
phonetics and interpretability experiments to understand the reasons for those
gaps (Q2). Phonetic analysis shows no significant differences across genders
and cannot explain the observed phenomenon. Interpretability through gender
probes reveals a negative correlation between female-male gaps and the ability
to discern data across genders from a model’s internal representations. This
finding suggests that when presented with speech from men and women, models
build different internal representations that can serve as a proxy for gender
disparities.

#### Contributions.

We conducted the first extensive evaluation of gender performance differences
between two widely used multilingual ASR models and found significant gender
gaps. We release code, data, and all artifacts we produce for future research.

#### Bias Statement.

When using gender as a variable, we focus on the speaker’s declared identity.
We evaluate whether speech from individuals identifying as female, male, or
neither of the two are equally processed by multilingual ASR models.
Conceptually, this is the ideal outcome, and we measure performance
disparities of minority groups (“female”, “other”) versus the socially
advantaged group, i.e., men. We define a system to be biased if it risks
further contributing allocational (i.e., quality of service, technology less
accessible) and representational harms impacting the minority groups, e.g.,
feeding into stereotypes about inadequacy of women and speech technology or
“shrill,” “incorrect” voice (Tallon, 2019).333Voice technology expert Tom
Schalk once said that “many issues with women’s voices could be fixed if
female drivers were willing to sit through lengthy training… Women could be
taught to speak louder and direct their voices towards the microphone.”
https://techland.time.com/2011/06/01/its-not-you-its-it-voice-recognition-
doesnt-recognize-women/

##  2 Background

#### Multilingual Speech Models.

Contemporary speech recognition systems based on Transformers (Vaswani et al.,
2017) outperform the previous transducer-based systems (Li et al., 2022).
Among these new systems, a particularly successful subset uses multi-task,
multilingual approaches. These architectures process audio and text with
separate Transformers and use different communication mechanisms to enable
cross-modal interactions. OpenAI’s Whisper (Radford et al., 2022) uses a
standard encoder-decoder architecture. Audio and text sequences can be
arbitrary—to differentiate among tasks and languages, it uses special decoding
tokens. This strategy allows multi-tasking and multilinguality without
architectural changes or fine-tuning (see Figure 1, top-right, in Radford et
al. (2022)). Similarly, Meta’s SeamlessM4T (Communication et al., 2023) uses a
speech encoder for audio and an encoder-decoder transformer for text (Costa-
jussà et al., 2022, NLLB).

While these models have shown strong multilingual capabilities, current
assessments primarily focus on overall performance metrics but lack, for
example, fairness evaluations in speech setups.444Seamless M4T (Communication
et al., 2023) has been previously evaluated for gender bias in translation
tasks.

#### Gender and Speech (Technologies).

Gendered aspects of the voice are one of the most salient individual traits
(Kreiman and Sidtis, 2011; Azul, 2015; Zimman, 2021) and have consequently
long been studied in linguistics (Zimman, 2020). The anatomy and makeup of the
vocal tract do play a role, as it determines pitch range and formant
variations, often regarded as the most distinctive phonetic vocal features of
(cis)gender men and women.555Cisgender refers to individuals whose gender
identity corresponds to the sex assigned at birth (Fuchs and Toda, 2010).
However, gender variation in the voice has been shown to arise from several
aspects besides physical ones (Oates and Dacakis, 2015; Zimman, 2018; Becker
et al., 2022). As studies including transgender individuals show Zimman
(2017), sociocultural factors influence vocal use, including which parts of
the pitch range are used (Loveday, 1981; Yuasa, 2008), and articulatory
practices for sibilant consonants and vowels that are perceived as gender
characterizing (Pharao et al., 2014; Podesva et al., 2016; Li, 2017).

For speech technologies, sociodemographic variation has posed challenges to
ASR systems (Sawalha and Abu Shariah, 2013; Liu et al., 2022; Rajan et al.,
2022; Fucci et al., 2023). Various works have found ASR models for English and
French to better recognize male speech and voices (Tatman, 2017; Garnerin et
al., 2019, 2021). This effect is often a result of the under-representation of
women in the training data (Meyer et al., 2020; Gaido et al., 2020; Garnerin
et al., 2020). However, various works have found the reverse to be true.
Several studies found comparatively better performance for women in more
spontaneous, conversational data in Dutch (Feng et al., 2022) and English
(Adda-Decker and Lamel, 2005; Koenecke et al., 2020). They attribute these
findings to sociolinguistic factors, like the higher incidence of disfluencies
and informal speech in men.

Thus, the current literature provides a fragmented picture of gender
disparities in speech technologies. However, most studies focus primarily on a
single language, usually English, albeit across multiple datasets and models.
We broaden this research to include large-scale multilingual speech models. In
addition, we evaluate performance in a third gender category, which includes
individuals who do not identify as male or female.

##  3 Methodology

This section describes the experimental design (§3) to answer Q1, i.e.,
whether there are gendered performance differences in multi-task multilingual
ASR models. Our results (§4) confirm it.

#### Models.

We experiment with OpenAI’s Whisper (Radford et al., 2022) and Meta’s
SeamlessM4T (Communication et al., 2023), two widely used, state-of-the-art
multilingual ASR models. See Appendix A.1 for implementation details.

#### Datasets.

Among other multilingual datasets available for ASR (Gales et al., 2014;
Black, 2019; Pratap et al., 2020; Iranzo-Sánchez et al., 2022; Valk and
Alumäe, 2021), we use Mozilla Common Voice (Ardila et al., 2020, CV),666We use
CV 16.0 from https://huggingface.co/datasets/mozilla-
foundation/common_voice_16_0 Fleurs (Conneau et al., 2023) and VoxPopuli (Wang
et al., 2021, VoxPopuli). Our choice of datasets is bound to the availability
of reliable speakers’ gender information.777Gender is self-declared in CV and
Fleurs, and retrieved from https://multimedia.europarl.europa.eu/en in
VoxPopuli. Furthermore, these datasets cover various recording and speech
conditions: i) read, where speech is typically well-articulated and based on
the reading of pre-defined texts (CV and Fleurs), and ii) spontaneous
conditions elicited from public talks and speeches, which allow for more
speaker-dependent articulations and variation in word usage (Gabler et al.,
2023, VoxPopuli). The type of recording conditions might influence the size of
performance disparities, with spontaneous speech providing a more realistic
image of the impact of potential disparities for real-use ASR cases.

To avoid unreliable results due to data contamination, we base our analysis on
the concatenation of validation and test splits of each dataset. See Appendix
A.4 for a discussion on data contamination and how transcription performance
varies across splits in our setup.

#### Languages.

We include 19 languages from CV and Fleurs, and a subset of those—11 in total,
due to data availability—from VoxPopuli. These languages represent diverse
language families and data availability conditions (i.e., high-low resource).
Our language selection depended on sufficient utterances stratified across
genders to allow for proper comparisons. See Table 4-6 for full statistics on
datasets in Appendix A.5.

###  3.1 Evaluation

#### Quality Metrics.

We use standard Word- and Character-Error Rate (WER and CER, respectively) to
evaluate transcription quality. Following Radford et al. (2022), we i) report
WER for all languages but Yoruba and Japanese—where variability in orthography
and ambiguous word units may affect evaluation Rowlands (1954); Matsuoka et
al. (1997)—for which we use CER, ii) normalize all references and hypotheses
texts by transliterating them into Cyrillic for Russian and Serbian, and iii)
when evaluating Whisper, we apply the official normalization routines to
texts.888Found at
https://github.com/openai/whisper/tree/main/whisper/normalizers Moreover, we
trim audio to the initial 30 seconds 999Most of the recordings in our datasets
are shorter than 30 seconds. See statistics in Table 4-6 in Appendix A.5. and
filter out dataset noise by removing records with an empty reference and
silence in the snippet.101010When processing Fleurs-es, we noticed some
records containing only background noise. We used state-of-the-art voice
activity detection models to detect and filter them out. See Appendix A.2 for
details.

When evaluated in terms of overall quality, both Whisper and Seamless showcase
competitive results, respectively, on: Fleurs (12.68 and 19.87 – avg. 19
langs); CV (17.51 and 16.42 – avg. 19 langs); VP (13.59 and 12.74 – avg. 11
langs). For disaggregated WER scores per each language and dataset, see Figure
7 in Appendix A.1.

#### Gap Metrics.

Measuring the ASR gender gap is equivalent to seeking what is commonly known
in the field of machine learning as group fairness (Chouldechova and Roth,
2020), or, more specifically, demographic parity (Dwork et al., 2012).
Conceptually, parity is achieved when a given statistical measure is equal
across different groups.

We operationalize demographic parity using the notation from Czarnowska et al.
(2021). We introduce a Pairwise Comparison Metric, loosely inspired by the
Disparity Score in Gaut et al. (2020), defined as follows:

$E(r_{A},r_{B})=100\cdot(\phi(r_{A})-\phi(r_{B}))/\phi(r_{B})$ (1)

where $r_{A}$ and $r_{B}$ are the set of audio snippets belonging to given
groups (e.g., either “male”, “female”, or “other” in CV), and $\phi(\cdot)$ is
one of the quality metrics defined above (i.e., WER or CER). The ideal score
is 0—i.e., the model performs perfectly equally on the two groups.111111ASR
models achieve different baseline error rates across languages. Equation 1
measures relative, rather than absolute, gaps to account for this variability.
See Table 3 in Appendix A.4 for details on absolute gaps and §7 for a
discussion on possible metric interpretations.

#### Significance Test.

We use a bootstrapped approach (Søgaard et al., 2014) to estimate
$\phi(\cdot)$. We run $n=1000$ iterations sampling 40% of the set. When
sampling, we balance the two groups in several records for a reliable
comparison. We compute $E(\cdot)$ on the arithmetic means of $\phi(\cdot)$
across the $n$ runs and use a two-sided Student’s t-test to compute the
statistical difference between the means.

Filtering and bootstrap sampling improve the evaluation robustness of the
skewed speaker and gender distributions. See Appendix A.4 for more details on
comparing sampling performance vs. complete sets.

Note that sampling requires attributing each record to an individual
speaker—information not available in Fleurs. For consistency, we attribute a
speaker ID to each record automatically. Specifically, we i) use state-of-the-
art speaker verification embedding models to encode each recording, ii)
cluster them using HDBSCAN (Campello et al., 2013), and iii) attribute a
speaker ID to each cluster. See Appendix A.3 for details on the pipeline.

##  4 Gender Performance Gaps

In the following, we report the results of evaluating Whisper and Seamless,
focusing on gender performance disparities. We first investigate whether
models are equally able to recognize speech for the female and male group
(§4.1), then for speakers that identify as neither of the two comparatively to
the male gender group (§4.2).

(a) VoxPopuli

(b) Fleurs

(c) CV

Figure 2: Model error rate gap (Eq. 1). Positive values indicate better
performance on men, negative ones on women. $p<.05$.

###  4.1 “Female” – “Male” Gap

Figure 2 reports $E(r_{F},r_{M})$, i.e., the gender gap results for Whisper
and Seamless on each dataset. Concerning our Q1, a bird’s eye view immediately
attests that multilingual models do not perform equally across female and male
speakers, showing a preference for one gender over the other for most
instances. Our analysis does not consistently reveal models disadvantaging the
feminine group—see values below 0. This finding stands in stark contrast with
mounting evidence of a strong masculine bias affecting a wide range of Natural
Language Processing (NLP) tasks (e.g., Sun et al., 2019; Stanczak et al.,
2022). It is especially noteworthy given the well-known under-representation
of women in current resources used for model training (Garnerin et al., 2019;
Zanon Boito et al., 2022; Sun and Peng, 2021). We observe that both speech
models—albeit to varying degrees—better recognize female speech on average in
two out of the three considered datasets (i.e., Fleurs and VP).

Upon closer examination, we also observe quite different behaviors across
datasets. First, despite the higher degree of spoken language variation to be
expected for the spontaneous VP dataset (Figure 2(a)), the evaluation of this
elicited resource shows a comparatively reduced gender gap, with overlapping
values between Whisper and Seamless for most languages (i.e., hu, es, fr, nl,
cs, de). Conversely, it is on read resource that we attest to a higher degree
of variability across models as well as languages, with occasional wide
performance disparities toward either females or males in CV (2(c)). Notably,
Seamless remains closer to performance equality for most instances on Fleurs
(2(b)), i.e., values between 11 and -10.

As such, in line with previous research (see §2), analysis of gender
disparities for the ASR reveals a complex picture, which we further analyze
and unpack in the following sections.

###  4.2 “Other” – “Male” Gap

| ca | de | en | es | fr
---|---|---|---|---|---
Whisper | 4.68 | 37.20 | 28.50 | -2.00 | 5.21
Seamless | 52.76 | 17.19 | 38.70 | 11.37 | -0.50
Table 1: Model error rate gap (Eq. 1). Positive values indicate better
performance on men, negative ones on “other.” $p<0.05$

Restricting our analysis to validation and test sets leaves us with few
records for speakers who do not identify as neither male nor female. As such,
we report results for five high-resource languages, Catalan, German, English,
Spanish, and French.121212Size varies from 16 (ca min.) to 86 (es, max)
records

Table 1 reports $E(r_{O},r_{M})$ for both models. Results suggest that models
penalize non-male speech and that gaps are larger than disparities observed
for the female/male groups. Given the limited sample size, we deem this
analysis only exploratory. However, it is a valuable first overview of the
model’s behaviors for gender-non-conforming voices. We underscore the need for
more representation of diverse voices and sociodemographic groups in current
resources, and for which monitoring fairness in existing spoken language
models remains out of reach. See §7 for an expanded discussion.

##  5 Acoustic Analysis

(a)

(b)

Figure 3: Acoustic features. Speaking rate, pitch average and intensity
average (left to right). Italian (3(a)) and Slovak (3(b)) on our three
datasets. “Male” (green), “Female” (red), and “Other” (blue, available for
CV).

Our findings (§4) reveal performance gaps across gender groups, with variable
trends over languages and datasets. To further investigate potential reasons,
we explore whether phonetic features, as measured across different groups in
our evaluation records, shed light on the phenomenon (Q2). For each segment,
we examine three parameters: i) pitch average, the mean of the fundamental
frequency values measured in Hertz (Hz) Hirst and de Looze (2021); ii)
intensity average, the mean of the fundamental frequency values measured in
decibel (dB) for each sample Pausewang Gelfer and Young (1997); iii) speaking
rate, computed as the number of tokens per minute Künzel (2013).131313We
compute average pitch and intensity using Praat Boersma and Weenink (2001).
Concerning speaking rate, we found no reliable multilingual tools for syllable
segmentation. Therefore, we approximated it by the number of tokens as
computed by Whisper’s tokenizer.

We analyze these features because they represent sociophonetic variability of
interest for the gender dimension and within our setting.

As already discussed in §2, differences in pitch between men and women are
well documented Coleman (1976); Hillenbrand and Clark (2009), and variations
in speaking rate have also been associated with gendered speech Borsel and
Maesschalck (2008). Besides, the selected features might capture distinctive
acoustic aspects across the datasets used in our experiments. For instance,
intensity can be influenced by the recording conditions Maryn and Zarowski
(2015), which can vary significantly in crowdsourced speech data.
Additionally, speech rate tends to be higher in spontaneous speech Nakamura et
al. (2008). Differences in acoustic parameters may also exist across
languages. For example, languages can vary in the average speaking rate Coupé
et al. (2019) and pitch Loveday (1981); Yuasa (2008).

#### Findings.

Since we found similar distributions across languages – and due to space
constrains – in Figure 3 we report results for two representative languages
only: Italian and Slovak. The former exemplifies the set of languages with
comparable performance across genders, whereas the latter is biased towards
men and belongs to the set of languages with larger gender gaps trends (§4).

By comparing these two groups of languages, pitch emerges at the only feature
that differ across female and male, whereas no substantial variation is
attested for intensity and speaking rate. Interestingly, the “other” group is
slightly more spread on pitch and intensity compared to “male” and “female”.
By comparing datasets, we still find similar patterns, except for the
intensity parameter. Intensity values are more divergent in their
distributions, which is particularly noticeable for Fleurs.141414This could be
attributed to the underlying recording conditions of Fluers, details of which
are however not provided.

Overall, however, we do not attest to significant acoustic differences across
datasets and languages, regardless of the gap size or direction (male, female,
other). Thus, these findings suggest that acoustic features cannot explain
gender-based performance gaps and their trends.

##  6 Probing Gender in ASR Models

Since models exhibit gender-based disparities and acoustic features do not
fully explain the phenomenon, we turn to interpretability analysis to
understand whether models encode records differently across genders.

The field of Natural Language Processing (NLP) has now established that
transformer language models encode syntactic (Hewitt and Manning, 2019),
semantic (Tenney et al., 2019), and factual (Petroni et al., 2019; Meng et
al., 2022) information in hidden representations. As such, recent work has
focused on extracting this information through probes, i.e., supervised
classifiers trained on the model’s embeddings (Alain and Bengio, 2016;
Belinkov and Glass, 2019). Compellingly, some have related extractability of
sensitive attributes, e.g., gender, to bias in downstream application (Orgad
et al., 2022). Probing is primarily motivated by the risk that models entangle
protected attributes and predictions in sensitive use cases (Zhao et al.,
2018; Ravfogel et al., 2020).

Motivated by this line of research, we measure gender extractability in ASR
models and ask (Q2) whether and to which extent it explains gender-based
performance gaps. Ours is the first study of this kind.151515Interpretability
for speech models is a relatively new research avenue. Mohebbi et al. (2023)
use context-mixing techniques to explain homophone disambiguation; Pastor et
al. (2023) use word-segment masking to explain semantic classication tasks.

#### Experimental design.

We focus on one dataset-model configuration, namely Whisper and CV. We attach
our probes to the model encoder’s last layer embeddings and train one distinct
probe for every position. We use Logistic Regression and Minimum Description
Length (MDL) (Voita and Titov, 2020) to probe gender in the female-male binary
setup (see Appendix A.6 for details).

Figure 4: F1 Macro (top) and measured code length ($1e^{6}$, bottom) for
Logistic Regression and MDL probes, respectively. English, F-M setup, first 10
seconds of context. Actual score (pale line), exponential moving average
(solid), and average length of test snippets (vertical dashed).

#### Findings.

Figure 4 shows gender extractability on the English CV split for the F-M
setup. Trends are similar across languages (full results in Figure 9(a)).
First, F1 scores suggest that gender extractability is relatively easy.
Whisper produces representations of female and male speech that can be easily
separated. Second, extractability is not constant over time. It starts high in
the first milliseconds, drops during the actual signal (i.e., the speaker is
talking), and finally plateaus around the initial value. This finding recalls
attention “sink” theory Xiao et al. (2023) implicating that transformers
prioritize initial positions for specific tasks. Third, MDL probes share
similar trends—low codelength indicates an “easier” task / higher
extractability—suggesting that simple logistic regression is a robust method
to start with, countering issues found instead in NLP (Hewitt and Liang, 2019;
Voita and Titov, 2020).161616To confirm this hypothesis, we conduct additional
experiments by training logistic probes with randomly shifted labels. We
report random guess performance on all languages (see Figure 9(b)) in Appendix
A.6.

#### Correlation with Error Rate.

Figure 5: Logistic probe F1S vs. $E(r_{F},r_{M})$. CV. Pearson’s correlation
(purple dashed line) is -0.65.

Do gender probing scores tell us something about ASR quality and gender bias
(Q2)? To answer this question, we compare F1 scores from logistic regression
scores and ASR error rates. We measure F1S, i.e., the average F1 score
achieved by probes at the positions corresponding to actual speech,171717We
approximate this range by considering all positions before the average test
set length (orange line in Figure 4). $\phi(r_{M})$ and $\phi(r_{F})$ (error
rates on male and female segments), and the disparity score $E(r_{F},r_{M})$.

F1S has a weak linear correlation with $\phi(r_{M})$ and $\phi(r_{F})$
(Pearson’s $\rho$ is 0.13 and -0.003, respectively), hinting that F1S cannot
explain per-group quality. However, $\rho$ between F1S and $E(r_{F},r_{M})$ is
-0.65 ($n=19$, see Figure 5). In other words, linear correlation suggests that
the better probes can extract gender from Whisper’s hidden states, the lower
the F-M error rate gap is. Note that, as per its definition, $E(r_{F},r_{F})$
can be negative (see points below the zero line in Figure 5). Negative values
indicate performance favoring the minority group (here, women). This finding
suggests that Whisper does encode recordings from speakers that identify as
men and women differently. This aspect can serve as a proxy for measuring and
mitigating gender disparities. However, we caution the reader from attributing
high extractability to gendered voice and discourage using probes on people to
predict gender (see §Ethical Considerations). Nonetheless, we argue that the
phenomenon can potentially serve as a proxy for measuring and mitigating
gender disparities.

##  7 Discussion

Our extensive analysis confirms that multi-task multilingual ASR models
display gender performance gaps. Depending on the language, disparities can
favor male or female speakers, but rarely those who do not identify as either.
In the following, we report two discussion points to help frame our study, its
findings, and its implications.

Figure 6: Whisper distribution of error rates. Female and male records in
Fleurs. nl (top) and ar (bottom). Proportion of records (y axis) having a WER
error rate (x axis) at least that large.

#### Interpreting $E(\cdot)$.

For our gender gap analysis, we study performance disparities by studying the
relative difference ($E(\cdot)$) in scores across gender groups (see §3.1).
While such definition allows to compare models and languages at different
absolute error levels, relative differences might tell little about the
concrete impact of gender disparities in ASR.181818For instance, a 10% F-M
relative difference corresponds to 0.2 or 2 WER points gap if we consider a
model that scores 2 or 20 absolute WER on men, respectively.  Ideally, a more
informative index would indicate the usability of an ASR system, thus
providing a more precise estimation of its failures and their severity.

Sentence-level error metrics can provide such a fine-grained view. Following
Koenecke et al. (2020), in Figure 6, we report the proportion of generated
transcriptions whose quality is greater than given WER values. Due to space
constraints, we only show the results for Whisper on Fleurs for two languages
as exemplars (i.e., Arabic and Dutch). This visualization lets us identify
where performance between genders starts to diverge. Let us hypothesize that
transcriptions became unusable after WER>20\. The plot can help us spot that
men and women would be equally affected in Dutch. However, 50% of Arabic
transcripts from men would be unsuitable compared to 40% of women’s. Parity
would be achieved if we lose the constraint up until around WER=40.

We echo the literature that urges the need for more informative/concrete bias
measurement (Delobelle et al., 2022; Orgad and Belinkov, 2022) and advocate
measures that can be used to guide model design and inform its reliable
deployment. Thus, this evaluation is particularly relevant and a more
realistic estimate of the impact of sociodemographic disparities in real-world
settings.

#### Group-Aware Dataset Splits.

Numerical and statistical studies like ours heavily depend on quality and
quantity of evaluation data. In this specific case, we would want to have
representative splits, i.e., records from many speakers from each group.
However, current dataset releases are drastically skewed. For instance, in
most of our datasets and language configurations, men are overrepresented in
the number of records and speakers. While downsampling can partially solve the
former at evaluation time, little can be done with the latter. Moreover, we
point out that stratification over gender and speakers to create splits
(train/validation/test) affects languages differently. In a high-resource
language, an 80/10/10 division still contributes many speakers from all groups
in validation and testing. However, in a low-resource setup, only a few
speakers are included—in CV most languages counted only one to 20 speakers
considering men and women, and frequently none or less than five speakers for
“other”.

We argue that dataset releases should factor in the difficulty in collecting
samples from less represented groups and provide group-aware splits to
facilitate fairness research.

##  8 Conclusion

We conducted the first extensive evaluation on gender-based performance gaps
of Whisper (Radford et al., 2022) and SeamlessM4T (Communication et al., 2023)
for ASR. These models consistently exhibit gender bias across 19 languages
from seven language families. Depending on the language, disparities can favor
men or women but rarely favor speakers who identify as neither. We locate a
potential source of these gaps using gender probes from interpretability
approaches. Our results show that probes can be a proxy for gender gaps and
that group fairness in multi-task multilingual models remains unsolved.

## Ethical Considerations

The use of gender as a variable in this warrants some ethical reflection.

As one of the most salient perceptual traits of one’s identity (Kreiman and
Sidtis, 2011; Azul, 2015; Zimman, 2021) gendered differences represent a
linchpin of much (socio)phonetic research (Zimman, 2020), which has unpacked
several physical and sociocultural factors contributing to such gendered
characteristics in the voice. Based on this evidence, our work does not intend
to be normative nor assumes the existence of a single, unidimensional “female”
or “male” voice. Instead, we question whether different gendered groups are
equally recognized by current multilingual ASR models and incorporate
sociophonetic knowledge in our analysis and discussions to isolate why that
might not be the case. To do so, we do not make any inference about the gender
of the speakers in the employed data. Instead, we rely on the declared gender
of the speakers in the employed speech resources. In this regard, part of our
analysis of the Mozilla Common Voice dataset uses a third category, “Other”,
which potentially aggregates diverse identities (e.g., transgender, non-
binary, and other marginalized individuals)191919These are among the offered
gender options that can currently be reported when donating one’s voice for
Common Voice. under one single umbrella term. While this third category also
allows us to include genders non-conforming to the binary in our study, we
recognize that this label and category might be an oversimplification and risk
erasing the experiences and representativity of many gender identities.

Finally, gender probes represent a methodological approach to studying how
models encode different audios from input signals. It is, hence, essential to
remember the inherent limitations and ethical implications of relying on
probes for gender classification. While these tools may offer a convenient
means of categorization and can be suitable for exploring the models’
behavior, they often overlook the nuanced and multifaceted nature of gender
identity.

## Limitations

Our paper comes with a series of limitations. We divide them into two
categories: data and methods.

#### Data.

Gender is the driving variable of our analysis. However, it is widely
recognized that gender interacts with other sociocultural factors, e.g.,
dialect (Wolfram, 2004) or sexual orientation (Zimman, 2013), in voice
production. By limiting to self-identified gender provided with the datasets,
our analysis can only provide a partial view. We thus advocate for speech
dataset releases that include a principled set of such factors.

The scarcity of data limits the generalizability of our results in two more
setups in our analysis. First, the category “Other” counts a deficient number
of speakers and records in most of our setups (see §7). Second, the number of
speakers we extracted from Fleurs is compared to CV and VoxPopuli (see 3.1).
These factors hamper cross-dataset comparison and overall generalizability.

We did not control for data quality on references. It might be that references
are noisy (e.g., single words, empty) and can lead to over- or under-
estimation of our measurements.

Finally, data contamination. Although we chose validation and test sets as our
analysis targets, we cannot exclude that models were trained on part or all of
them. We conducted a side analysis (Appendix A.4) that suggested that this
might not be the case.

#### Methods.

Since we miss reliable multilingual phonetic tools, we estimated the speaking
rate using Whisper’s pre-trained tokenizer. We acknowledge that pretrained
tokenizers yield fewer tokens for high-resource languages. Hence, our
measurement of low-resource might be overestimating the phenomenon.

If framed in the context of bias evaluation paradigms—which we are currently
not doing—probing can be seen as an intrinsic bias paradigm. We correlate
probing performance to a downstream task (and potential harm), i.e., quality
gaps in ASR. However, studies in the field of NLP recognize that intrinsic and
extrinsic (downstream) bias metrics do not necessarily correlate (Goldfarb-
Tarrant et al., 2021; Kaneko et al., 2022). To our knowledge, ours is the
first study to inspect such aspects in transformers for speech, and findings
may not hold across modalities. We leave this research question to future
work.

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##  Appendix A Experimental Details

(a) VoxPopuli

(b) Fleurs

(c) Common Voice

Figure 7: Seamless and Whisper transcription quality. WER results are computed
on test splits.

###  A.1 Multilingual ASR Models

For both Whisper and SeamlessM4T, we used code and model checkpoints in
transformers (Wolf et al., 2020). The Hub’s model IDs are openai/whisper-
large-v3 and facebook/seamless-m4t-v2-large, respectively. Both checkpoints
correspond to the latest and best-performing versions available at the time of
writing, February 2024.

We used each models’s standard decoding configurations to transcribe each
audio snippet.

In figure 7, we report WER results for Seamless and Whisper on the languages
and datasets used in our experiments.

|  | test | train | val | gender set | gender subset
---|---|---|---|---|---|---
Fleurs | Whisper | 12.68${\pm.11.3}$ | 12.64${\pm.11.4}$ | 12.49${\pm.11}$ | 12.60${\pm.11.2}$ | 12.62${\pm.11.2}$
| S 4MT | 19.87${\pm.6.4}$ | 19.72${\pm.6.3}$ | 18.75${\pm.6.2}$ | 19.65${\pm.6.3}$ | 19.67${\pm.6.3}$
CV | Whisper | 17.51${\pm.17.5}$ | 16.65${\pm.19.8}$ | 17.02${\pm.18.7}$ | 16.88${\pm.19.2}$ | 18.45${\pm.19.2}$
| S M4T | 16.42${\pm.9.5}$ | 12.68${\pm 9.9}$ | 14.7${\pm.8.9}$ | 13.06${\pm.9.6}$ | 15.58${\pm.9.3}$
VP | Whisper | 13.59${\pm.4.45}$ | 12.63${\pm.3.79}$ | 12.99${\pm.4.22}$ | 12.69${\pm.3.8}$ | 13.32${\pm.4.2}$
| S M4T | 12.74${\pm.3.27}$ | 11.27${\pm.2.63}$ | 12.33${\pm.2.68}$ | 11.39${\pm.2.6}$ | 12.54${\pm.2.9}$
Table 2: WER across datasets splits (test/train/validation), all presampled female/male records, and on the sampled gender subset used in our experiments. Results averaged over 19 languages in CV and Fleurs, and 11 for VoxPopuli. |  | test | train | val | presample_all | overall
---|---|---|---|---|---|---
Fleurs | Whisper | -1.18${\pm.2.9}$ | -0.659${\pm.1.8}$ | -0.255${\pm.2.6}$ | -0.893${\pm.1.7}$ | -0.997${\pm.1.9}$
| S M4T | 0.153${\pm.1.9}$ | -0.880${\pm.1}$ | -0.257${\pm.2.7}$ | -0.735${\pm.0.72}$ | -0.809${\pm.0.72}$
CV | Whisper | 1.17${\pm.5.7}$ | -0.65${\pm.2.7}$ | 0.88${\pm.5.6}$ | -0.14${\pm.3.5}$ | 1.06${\pm.5.3}$
| S M4T | 2.01${\pm.5.4}$ | -1.22${\pm.5.9}$ | 0.40${\pm.2.7}$ | 0.59${\pm.2.9}$ | 1.05${\pm.3.3}$
VP | Whisper | 0.31${\pm.3.5}$ | -0.80${\pm.0.97}$ | -0.58${\pm.1.9}$ | -0.73${\pm.0.9}$ | -0.104${\pm.2.2}$
| S M4T | 0.16${\pm.2.8}$ | -0.77${\pm.0.54}$ | -0.71${\pm.2.1}$ | -0.75${\pm.0.6}$ | -0.268${\pm.2.2}$
Table 3: Average F-M WER absolute difference across datasets splits
(test/train/validation), all presampled female/male records, and on the
sampled gender subset used in our experiments. The results are averaged over
19 languages for CV and 11 for VoxPopuli. For Fleurs, we average results over
the only 9 languages that comprise both female/male speakers in all splits.

###  A.2 Voice Activity Detection

While conducting our experiments, we noticed that other than records with an
empty reference, datasets can contain also empty audio snippets. More
precisely, these recordings do have a signal but it is mostly silence. We
found this phenomenon primarily in Fleurs-es, with silence mostly coming from
snippets attributed to women.

Therefore, we used pyannote.audio’s pretrained neural models and code for
voice activity detection.202020https://github.com/pyannote/pyannote-audio
After counting the number of segments where voice was detected, we filter out
all snippets were no segment was detected. We release the the counts and IDs
of silence records in our repository.

###  A.3 Speaker ID Attribution in Fleurs

Figure 8: UMAP projections of snippets from men (left, 13 speakers found) and
women (right, 16 speakers found) in Fleurs-en. Color indicates cluster ID
assigned by HDBSCAN.

Speaker IDs are crucial in our pipeline to avoid over- or under-estimating
performance gaps due to overly present speakers. Since Fleurs does not provide
this piece of information, we attribute it automatically.

Specifically, we encode each snippet using a pretrained ECAPA-TDNN model for
speaker verification. We use code from SpeechBrain (Ravanelli et al., 2021)
and the model checkpoint with Hub ID speechbrain/spkrec-ecapa-voxceleb. Next,
we cluster embeddings separately per gender using HDBSCAN (Campello et al.,
2013), with code from scikit-learn. We use set min_cluster_size to 2 and use
cosine similarity. Finally, we assign a numerical ID to each found cluster.
Figure 8 shows an example of clustered snippets projected with UMAP (McInnes
et al., 2018). We release per dataset and language IDs in our repository.

###  A.4 Subset selection and data splits

To ensure the soundness of our experimental framework and of the gender
subsample selection described in in Section 3, we conducted preliminary
analyses by comparing WER transcription quality scores (Table 2) and gender
F-M gap results (Figure 3) across: i) different datasets splits
(test/train/validation), ii) all female/male records available from any split,
and iii) the sampled female/male subset used in our main experiments.

Since no precise information concerning the data used to train Whisper and
Seamless is publicly available, this evaluation is carried out with the goal
of spotting potential data contamination issues. Namely, to ensure that we are
not testing models on data samples comprised in their underlying training,
thus posing the risk of obtaining unreliable results, which cannot be compared
across datasets and models.

Figure 2 shows no notable WER variations for Fleurs. Instead, both models
achieve better performance on the train splits of CV and VP, especially for
the former dataset. The influence of the split accounted in CV and VP can thus
be reflective of the F-M WER differences reported in Figure 3. Informed by
such results, the gender subset used in our experiments is sampled from the
test and validation split only for CV and VP, whereas for Fleurs we leverage
gender data from the whole corpus.

###  A.5 Dataset Statistics

Lang | # records | Seconds | # Tokens | # M | Gini (M) | # F | Gini (F)
---|---|---|---|---|---|---|---
ar | 14013 | 4.37$\pm 1.51$ | 19.92$\pm 17.55$ | 319 | 0.66 | 102 | 0.68
ca | 1645 | 6.12$\pm 1.73$ | 17.05$\pm 6.22$ | 265 | 0.22 | 241 | 0.22
cs | 13746 | 4.60$\pm 1.38$ | 20.77$\pm 8.71$ | 293 | 0.64 | 39 | 0.61
de | 3641 | 6.10$\pm 1.75$ | 16.74$\pm 6.08$ | 807 | 0.22 | 129 | 0.24
en | 4938 | 5.96$\pm 2.28$ | 11.08$\pm 3.99$ | 2262 | 0.25 | 476 | 0.25
es | 6404 | 6.09$\pm 1.60$ | 15.61$\pm 5.01$ | 1476 | 0.29 | 517 | 0.30
fi | 1695 | 4.74$\pm 1.66$ | 18.22$\pm 8.93$ | 52 | 0.57 | 17 | 0.57
fr | 4214 | 5.81$\pm 1.67$ | 16.40$\pm 5.56$ | 876 | 0.19 | 213 | 0.19
hu | 12245 | 5.45$\pm 1.52$ | 22.84$\pm 9.23$ | 173 | 0.57 | 277 | 0.53
it | 7088 | 6.14$\pm 1.69$ | 17.52$\pm 6.44$ | 888 | 0.29 | 203 | 0.28
ja | 7981 | 5.02$\pm 2.29$ | 22.64$\pm 15.07$ | 1033 | 0.44 | 632 | 0.41
nl | 12808 | 4.83$\pm 1.38$ | 16.29$\pm 5.57$ | 487 | 0.62 | 130 | 0.63
pt | 7319 | 4.61$\pm 1.63$ | 10.66$\pm 5.44$ | 620 | 0.41 | 82 | 0.37
ro | 6489 | 4.05$\pm 0.90$ | 15.70$\pm 4.13$ | 167 | 0.56 | 39 | 0.57
ru | 10466 | 5.51$\pm 1.85$ | 18.87$\pm 9.18$ | 702 | 0.45 | 248 | 0.43
sk | 3008 | 4.34$\pm 1.74$ | 13.66$\pm 9.64$ | 58 | 0.64 | 13 | 0.65
sr | 1895 | 2.86$\pm 0.89$ | 8.63$\pm 5.97$ | 39 | 0.57 | 11 | 0.64
sw | 13730 | 5.61$\pm 1.80$ | 21.04$\pm 8.13$ | 248 | 0.62 | 251 | 0.58
yo | 1226 | 5.77$\pm 1.40$ | 41.97$\pm 9.89$ | 14 | 0.48 | 20 | 0.64
Table 4: Statistic on the sample used for our analysis (validation and test) for Mozilla Common Voice. Total number of records, average length in seconds and number of tokens as tokenized by Whisper’s pretrained tokenizer, number of male and female speakers, and gini dispersion index of the snippets-per-speaker dispersion, separately by subset.  Lang | # records | Seconds | # Tokens | # M | Gini (M) | # F | Gini (F)
---|---|---|---|---|---|---|---
cs | 2208 | 9.93$\pm 5.38$ | 60.04$\pm 31.51$ | 45 | 0.58 | 16 | 0.47
de | 4064 | 8.74$\pm 5.87$ | 32.34$\pm 20.82$ | 147 | 0.55 | 64 | 0.57
en | 3485 | 9.92$\pm 6.17$ | 24.48$\pm 15.05$ | 274 | 0.52 | 104 | 0.52
es | 3130 | 11.56$\pm 6.78$ | 42.89$\pm 25.15$ | 95 | 0.57 | 48 | 0.48
fi | 1141 | 9.99$\pm 5.44$ | 46.65$\pm 26.61$ | 25 | 0.63 | 16 | 0.45
fr | 3341 | 10.23$\pm 6.15$ | 38.19$\pm 23.92$ | 151 | 0.52 | 84 | 0.54
hu | 2145 | 10.47$\pm 6.05$ | 62.48$\pm 35.59$ | 45 | 0.48 | 21 | 0.50
it | 2419 | 13.00$\pm 7.19$ | 52.78$\pm 28.99$ | 87 | 0.47 | 37 | 0.53
nl | 2351 | 8.16$\pm 5.23$ | 36.63$\pm 22.88$ | 69 | 0.54 | 42 | 0.54
ro | 2701 | 11.29$\pm 5.75$ | 57.31$\pm 31.54$ | 51 | 0.52 | 19 | 0.57
sk | 1266 | 10.33$\pm 5.55$ | 62.52$\pm 35.16$ | 26 | 0.57 | 10 | 0.47
Table 5: Statistic on the sample used for our analysis (validation and test) for VoxPopuli. Total number of records, average length in seconds and number of tokens as tokenized by Whisper’s pretrained tokenizer, number of male and female speakers, and gini dispersion index of the snippets-per-speaker dispersion, separately by subset.  Lang | # records | Seconds | # Tokens | # M | Gini (M) | # F | Gini (F)
---|---|---|---|---|---|---|---
ar | 836 | 10.83$\pm 4.08$ | 57.52$\pm 21.70$ | 9 | 0.31 | 38 | 0.51
ca | 2914 | 11.87$\pm 4.00$ | 39.93$\pm 14.87$ | 5 | 0.15 | 5 | 0.05
cs | 3772 | 12.05$\pm 4.45$ | 53.38$\pm 19.93$ | 7 | 0.36 | 5 | 0.21
de | 3326 | 12.98$\pm 6.32$ | 37.19$\pm 13.30$ | 4 | 0.07 | 4 | 0.47
en | 2832 | 9.75$\pm 3.58$ | 24.55$\pm 8.54$ | 13 | 0.33 | 15 | 0.28
es | 3082 | 12.16$\pm 3.80$ | 37.88$\pm 13.67$ | 6 | 0.38 | 7 | 0.35
fi | 3482 | 12.74$\pm 4.43$ | 47.38$\pm 18.27$ | 12 | 0.34 | 18 | 0.47
fr | 3194 | 10.25$\pm 3.58$ | 38.80$\pm 15.06$ | 4 | 0.06 | 6 | 0.15
hu | 4374 | 12.09$\pm 4.12$ | 54.90$\pm 21.68$ | 5 | 0.03 | 5 | 0.05
it | 3528 | 14.53$\pm 5.34$ | 40.59$\pm 14.30$ | 6 | 0.33 | 6 | 0.31
ja | 2348 | 12.99$\pm 3.77$ | 49.50$\pm 18.25$ | 6 | 0.50 | 16 | 0.66
nl | 2594 | 9.55$\pm 3.73$ | 40.59$\pm 14.63$ | 8 | 0.53 | 6 | 0.23
pt | 3944 | 12.50$\pm 4.24$ | 35.32$\pm 12.88$ | 5 | 0.08 | 4 | 0.19
ro | 4022 | 10.22$\pm 3.63$ | 48.98$\pm 18.03$ | 5 | 0.08 | 7 | 0.35
ru | 3496 | 11.39$\pm 3.97$ | 42.57$\pm 16.14$ | 13 | 0.36 | 11 | 0.26
sk | 2600 | 11.65$\pm 3.81$ | 53.15$\pm 19.93$ | 8 | 0.41 | 10 | 0.57
sr | 2820 | 10.76$\pm 3.40$ | 59.46$\pm 21.59$ | 4 | 0.07 | 6 | 0.08
sw | 3588 | 14.08$\pm 4.52$ | 49.60$\pm 17.25$ | 18 | 0.42 | 17 | 0.39
yo | 2850 | 16.32$\pm 5.66$ | 73.91$\pm 34.05$ | 7 | 0.36 | 6 | 0.50
Table 6: Statistic on the sample used for our analysis (validation and test)
for Fleurs. Total number of records, average length in seconds and number of
tokens as tokenized by Whisper’s pretrained tokenizer, number of male and
female speakers, and gini dispersion index of the snippets-per-speaker
dispersion, separately by subset. Speakers extracted automatically.

Table 4, 6, and 5 reports all collected statistics on CV, Fleurs, and
VoxPopuli, respectively.

###  A.6 Details on Gender Probing

Training and test set for probing experiments are sampled from the
concatenation of each language’s validation and test sets for consistency with
error rate experiments. We balanced the samples on gender, stratifying on the
speaker distribution.

We used standard logistic regression probes as per scikit-learn’s
implementation and standard parameters but a larger allowance of steps for
convergence (n=1000). For MDL probes, we used logistic regression as the
backbone classifier for comparability and operationalized the probe via the
online code configuration Voita and Titov (2020). Following previous work
(Orgad et al., 2022), we set the dataset slices to (percentage): [0.2, 0.4,
0.8, 1.6, 3.2, 6.25, 12.5, 25, 50, 100].

We probe a total of 1000 distinct positions that correspond roughly to the
first tens seconds of each recording (Whisper encodes audio with 25
milliseconds-long rolling frame and stride of 10 milliseconds) (Radford et
al., 2022).

###  A.7 Energy Statement

Experiments were conducted using a private infrastructure on hardware of type
A100 PCIe 80GB (TDP of 250W) with carbon efficiency of 0.233
kgCO2eq/kWh.212121https://app.electricitymaps.com Consumption is mostly due to
inference to generate transcripts. We estimate a total emission of 3.6 kgCO2eq
for experiments in the main body (transcripts of test and validation sets).
Transcripts on training sets account for additional 104.24 kgCO2eq. No
emission was directly offset. Total emissions are equivalent to driving an
internal combustion engine car for 980 km.222222Estimate based on average CO2
emissions of new passenger cars in EU in 2020.
https://www.acea.auto/figure/average-co2-emissions-of-new-cars-in-eu Estimate
was conducted using codecarbon232323https://github.com/mlco2/codecarbon but
does not account for energy for cooling the infrastructure.

##  Appendix B Artifacts Statement

The following is a list of artifacts we produced in this work that we release
to facilitate future research.

  * •

Transcriptions obtained with Whisper and SeamlessM4T of the full datasets
Mozilla Common Voice, Google Fleurs, and Meta VoxPopuli.

  * •

Per-record annotations of segments with voice activity extracted from the
voice activity detection pipeline (§A.2).

  * •

Per-record acoustic features in our phonetic study (§5).

  * •

Per-record embeddings and cluster IDs extracted on Fleurs with the speaker
identification pipeline (§A.3).

  * •

Extensive statistics on speaker, gender, and record distributions on the three
datasets—which we could not find anywhere else online.

  * •

Dataset samples on which we computed the quality and gap metrics for
reproducibility purposes (§3).

(a) Probes trained on untouched labels.

(b) Probes trained on shuffled labels.

Figure 9: F-M gender probing F1 Macro performance for every contextual
embedding in Whisper-large-v3 within the first 10 seconds (x axis). Logistic
regression probe with L2 regularization trained on standard (9(a)) and
shuffled (9(b)) training labels. Orange lines indicate the average length of
test segments. All CV languages.