pith. sign in

arxiv: 2604.22631 · v1 · submitted 2026-04-24 · 💻 cs.CL

Identifying and typifying demographic unfairness in phoneme-level embeddings of self-supervised speech recognition models

Felix Herron , Solange Rossato , Alexandre Allauzen , Fran\c{c}ois Portet This is my paper

Pith reviewed 2026-05-08 11:38 UTC · model grok-4.3

classification 💻 cs.CL
keywords demographic fairnessphoneme embeddingsself-supervised ASRspeaker groupsembedding biasrandom variancespeech recognition unfairnessprobe training
0
0 comments X

The pith

Phoneme embeddings in speech models contain both random variance and systematic bias that disadvantage certain speaker groups.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper develops a way to separate two kinds of problems in how self-supervised speech models represent individual sounds for different demographic groups. It shows that training a simple phoneme classifier using only data from one underperforming group can boost accuracy for that group, pointing to systematic bias in the embeddings. At the same time, groups with more variable embeddings also show poorer classification results, indicating that random error plays a role. This matters because fairer ASR systems require knowing whether to target bias correction or variance reduction.

Core claim

Both types of error—random high-variance embeddings and systematic embedding bias—are present in phoneme-level representations of self-supervised ASR models. Evidence for bias comes from performance gains when phoneme probes are trained on a single disadvantaged speaker group, while random error is shown by the link between higher phoneme variance and lower prediction accuracy. Random error appears to be the larger contributor to demographic unfairness, and standard fairness finetuning does not mitigate either.

What carries the argument

A typification framework distinguishing random error (high variance in phoneme embeddings) from systematic error (embedding bias), measured through phoneme classification probes trained on data from single speaker groups.

If this is right

  • Both random variance and systematic bias in phoneme embeddings are candidate causes of speaker group unfairness in ASR.
  • Random error likely hinders fairness more than systematic bias.
  • Speakers and groups with higher phoneme embedding variance show worse phoneme prediction accuracy.
  • Finetuning encoders with domain enhancing and adversarial training leaves both the probe benefits and variance levels unchanged.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Methods focused on reducing embedding variance, such as regularization during pretraining, might improve fairness more effectively than current adversarial approaches.
  • The probe training technique could serve as a diagnostic tool for measuring demographic bias in other embedding-based systems without retraining the full model.
  • Future work might test whether these error types appear similarly in other languages or non-speech audio tasks.

Load-bearing premise

That gains in phoneme probe accuracy from training exclusively on a disadvantaged speaker group reflect bias in the pretrained embeddings rather than artifacts introduced by the probe itself or data selection.

What would settle it

An experiment showing no improvement in phoneme classification when probes are trained only on data from a low-performing speaker group, or finding no correlation between measured phoneme variance and classification error rates across groups.

Figures

Figures reproduced from arXiv: 2604.22631 by Alexandre Allauzen, Felix Herron, Fran\c{c}ois Portet, Solange Rossato.

Figure 1
Figure 1. Figure 1: Toy visualization of high variance vs embed view at source ↗
Figure 2
Figure 2. Figure 2: Absolute F1 score at every layer for each view at source ↗
Figure 3
Figure 3. Figure 3: , the baseline is average F1 over all SGs; for view at source ↗
Figure 5
Figure 5. Figure 5: shows the absolute KNN distance for all phonemes across all layers for ASR finetuned models. Note how the KNN distance decreases layer by layer over the first several model layers for every phoneme and model - this is logical, given that PR accuracy tends to increase over these layers, thus distance between embeddings should decrease (Pasad et al., 2022) view at source ↗
Figure 4
Figure 4. Figure 4: Macro F1 phoneme classification scores, rel￾ative to probe training on a balanced dataset for SGs from the corresponding demographic variable for ASR￾finetuned S3Ms. Values > 0 indicate that training on that SG results in better performance than for a bal￾anced dataset (e.g. top left: when phoneme classifiers are trained on only men, men have a higher PR than for probe training on balanced data). Horizonta… view at source ↗
Figure 7
Figure 7. Figure 7: Relationship between KNN distance and view at source ↗
Figure 6
Figure 6. Figure 6: Relative KNN distance between embeddings view at source ↗
Figure 8
Figure 8. Figure 8: Difference in relative (to balanced training view at source ↗
Figure 10
Figure 10. Figure 10: 2-dimensional PCA decompositions for phoneme embeddings for speaker 440 for four phonemes for view at source ↗
Figure 12
Figure 12. Figure 12: Macro F1 phoneme classification scores, relative to probe training on a balanced dataset for SGs from the corresponding demographic variable, for pretrained S3Ms. Values > 0 indicate that training on that SG results in better performance than for a bal￾anced dataset (e.g. top left: when phoneme classifiers are trained on only men, men have a higher PR than for probe training on balanced data). Horizontal … view at source ↗
Figure 11
Figure 11. Figure 11: Macro F1 phoneme classification scores, relative to the macro average over all SGs (e.g. men and women) for the corresponding demographic vari￾able (e.g. gender) for pretrained S3Ms. Values > 0 in￾dicate that SG has a better-than-average macro F1 (e.g. top left: when trained on all data, females have above￾average macro F1 performance in layer 0). Horizontal lines on top and bottom of each figure denote s… view at source ↗
Figure 13
Figure 13. Figure 13: Relative KNN distance between embed￾dings of the same phoneme for the same speaker at each layer of pretrained S3Ms. 16 view at source ↗
Figure 14
Figure 14. Figure 14: Macro F1 phoneme classification scores, relative to probe training on a balanced dataset for SGs from the corresponding demographic variable, for each phoneme separately, aggregated over the best-performing layers of all six encoder models. Values > 0 indicate that training on that SG results in better performance than for a balanced dataset. Each phoneme has six bars, one per encoder model. * represents … view at source ↗
Figure 15
Figure 15. Figure 15: Relative macro F1 probing accuracy for each demographic variable (DV) based on embeddings from various phonemes. Red line is absolute average F1 over all phonemes. Scores < 0 means less SG infor￾mation is present for any given phoneme/layer than the average over all phonemes at that layer. tion from each phoneme embedding so that the classifier cannot use it in discriminating between SGs. One strategy to … view at source ↗
read the original abstract

Modern automatic speech recognition (ASR) systems have been observed to function better for certain speaker groups (SGs) than others, despite recent gains in overall performance. One potential impediment to progress towards fairer ASR is a more nuanced understanding of the types of modeling errors that speech encoder models make, and in particular the difference between the structure of embeddings for high-performance and low-performance SGs. This paper proposes a framework typifying two types of error that can occur in modeling phonemes in ASR systems: random error/high variance in phoneme embedding, vs systematic error/embedding bias. We find that training phoneme classification probes only on a single, typically disadvantaged SG, sometimes improves performance for that SG, which is evidence for the existence of SG-level bias in phoneme embeddings. On the other hand, we find that speakers and SGs with higher levels of phoneme variance are the same as those with worse phoneme prediction accuracy. We conclude that both types of error are present in phoneme embeddings and both are candidate causes for SG-level unfairness in ASR, though random error is likely a greater hindrance to fairness than systematic error. Furthermore, we find that finetuning encoder models using a fairness-enhancing algorithm (domain enhancing and adversarial training) changes neither the benefits of in-domain phoneme classification probe training, nor measured levels of random embedding error.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper proposes a framework for distinguishing random error (high variance in phoneme embeddings) from systematic error (SG-level embedding bias) in self-supervised ASR models. It reports that phoneme probes trained on a single disadvantaged speaker group (SG) sometimes improve performance for that SG, taken as evidence of systematic bias in the embeddings; it also finds that higher phoneme variance correlates with worse prediction accuracy, suggesting random error is the larger contributor to unfairness. Fairness-enhancing finetuning (domain adaptation and adversarial training) is shown to leave both patterns unchanged.

Significance. If the central experimental claims hold after controls, the typology offers a useful diagnostic lens for phoneme-level sources of demographic unfairness in ASR, separating variance-driven from bias-driven mechanisms. The probe-based detection method and the negative result on standard fairness finetuning are practical contributions that could guide embedding-specific interventions rather than post-hoc fixes.

major comments (2)
  1. [Probe training results (experiments section describing single-SG probes)] The key evidence for systematic error—that single-SG probe training improves performance for the disadvantaged SG—is presented without isolating the contribution of the frozen encoder embeddings. The probe stage itself can adapt to SG-specific acoustics, label distributions, or selection effects; without reported cross-SG probe evaluation, balanced mixed-data training ablations, or capacity-matched controls, the gain cannot be confidently attributed to pre-existing embedding bias rather than probe adaptation. This directly underpins the claim that systematic error is present and a candidate cause of SG-level unfairness.
  2. [Abstract and methods/experiments sections] The abstract states that findings support the presence of both error types and that random error is likely greater, yet provides no details on datasets, number of SGs/speakers, statistical tests, error bars, or exclusion criteria. The soundness of the variance-accuracy correlation and the finetuning invariance claims cannot be evaluated without these; if the full paper omits them, the comparative conclusion that random error is the greater hindrance rests on unverified experimental outcomes.
minor comments (1)
  1. [Introduction and framework section] Notation for speaker groups (SGs) and phoneme variance measures could be defined more explicitly on first use to aid readers unfamiliar with the ASR fairness literature.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback on our manuscript. We address each of the major comments below, providing clarifications and indicating planned revisions to strengthen the paper.

read point-by-point responses

  1. Referee: [Probe training results (experiments section describing single-SG probes)] The key evidence for systematic error—that single-SG probe training improves performance for the disadvantaged SG—is presented without isolating the contribution of the frozen encoder embeddings. The probe stage itself can adapt to SG-specific acoustics, label distributions, or selection effects; without reported cross-SG probe evaluation, balanced mixed-data training ablations, or capacity-matched controls, the gain cannot be confidently attributed to pre-existing embedding bias rather than probe adaptation. This directly underpins the claim that systematic error is present and a candidate cause of SG-level unfairness.

    Authors: We agree that additional controls are necessary to more confidently attribute the performance gains to biases in the frozen embeddings rather than adaptations during probe training. In the revised manuscript, we will include cross-SG probe evaluations (training on one SG and testing on others), balanced mixed-data training ablations, and capacity-matched controls. These additions will help isolate the contribution of the pre-existing embedding structure. We believe this will support our claim while addressing the concern. revision: yes

  2. Referee: [Abstract and methods/experiments sections] The abstract states that findings support the presence of both error types and that random error is likely greater, yet provides no details on datasets, number of SGs/speakers, statistical tests, error bars, or exclusion criteria. The soundness of the variance-accuracy correlation and the finetuning invariance claims cannot be evaluated without these; if the full paper omits them, the comparative conclusion that random error is the greater hindrance rests on unverified experimental outcomes.

    Authors: The full manuscript provides the requested details in the methods and experiments sections, including dataset descriptions, the number of speaker groups and speakers, statistical tests performed, error bars on figures, and exclusion criteria. However, we acknowledge that the abstract is too concise and omits these specifics. In the revision, we will expand the abstract to include key information such as the datasets used, number of SGs, and references to the statistical analyses and error reporting. This will allow readers to better evaluate the claims regarding the relative impact of random versus systematic error and the finetuning results. revision: yes

Circularity Check

0 steps flagged

No circularity; claims rest on independent experimental measurements

full rationale

The paper derives its conclusions from direct experimental results: performance gains when phoneme probes are trained on single disadvantaged speaker groups, and correlations between phoneme variance and prediction accuracy. These measurements are reported as observations rather than derived from definitions or prior self-citations that presuppose the target claims. No equations reduce outputs to inputs by construction, no fitted parameters are relabeled as predictions, and no uniqueness theorems or ansatzes are imported via self-citation. The derivation chain is therefore self-contained against the reported data.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

Based solely on the abstract, the work relies on standard domain assumptions in embedding analysis without introducing new free parameters or invented entities.

axioms (2)
  • domain assumption Phoneme classification probes trained on embeddings can reveal underlying bias or variance properties of those embeddings
    Invoked when using probe performance to infer SG-level bias and when linking variance to accuracy.
  • domain assumption Defined speaker groups (SGs) are meaningful categories for measuring demographic unfairness in ASR
    Used to categorize performance differences and interpret probe results.

pith-pipeline@v0.9.0 · 5555 in / 1251 out tokens · 47379 ms · 2026-05-08T11:38:09.885348+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

7 extracted references · 7 canonical work pages · 1 internal anchor

  1. [1]

    Exploring simple siamese representation learning, 2020

    WavLM: Large-Scale Self-Supervised Pre- Training for Full Stack Speech Processing . IEEE Journal of Selected Topics in Signal Processing , 16(6):1505–1518. Xinlei Chen and Kaiming He. 2020. Exploring Sim- ple Siamese Representation Learning . Preprint, arXiv:2011.10566. Kwanghee Choi, Ankita Pasad, Tomohiko Nakamura, Satoru Fukayama, Karen Livescu, and Sh...

    work page arXiv 2020

  2. [2]

    In The 2024 ACM Conference on Fairness, Accountability, and Transparency , pages 1672–1681

    Careless Whisper: Speech-to-Text Halluci- nation Harms . In The 2024 ACM Conference on Fairness, Accountability, and Transparency , pages 1672–1681. Jialu Li, Vimal Manohar, Pooja Chitkara, Andros Tjandra, Michael Picheny, Frank Zhang, Xiaohui Zhang, and Y atharth Saraf. 2021. Accent-Robust Automatic Speech Recognition Using Supervised and Unsupervised Wa...

    work page arXiv 2024

  3. [3]

    2022 , month = dec, journal =

    Layer-wise Analysis of a Self-supervised Speech Representation Model . Preprint, arXiv:2107.04734. Ankita Pasad, Bowen Shi, and Karen Livescu. 2023. Comparative layer-wise analysis of self-supervised speech models. Preprint, arXiv:2211.03929. Eliana Pastor, Alkis Koudounas, Giuseppe Attanasio, Dirk Hovy, and Elena Baralis. 2024. Explaining Speech Classific...

    work page arXiv 2023

  4. [4]

    Journal of Speech, Language, and Hearing Research, 63(2):533–551

    How Does Our V oice Change as We Age? A Systematic Review and Meta-Analysis of Acous- tic and Perceptual V oice Data From Healthy Adults Over 50 Y ears of Age. Journal of Speech, Language, and Hearing Research, 63(2):533–551. Chloe Sekkat, Fanny Leroy, Salima Mdhaffar, Blake Perry Smith, Y annick Estève, Joseph Dureau, and Alice Coucke. 2024. Sonos V oice...

    work page arXiv 2024

  5. [5]

    In Interspeech 2008 , pages 2550–

    Longitudinal study of ASR performance on ageing voices . In Interspeech 2008 , pages 2550–

    work page 2008

  6. [6]

    A Study of Data Selection Strategies for Pre-training Self-Supervised Speech Models

    ISCA. Angelina Wang, Vikram V . Ramaswamy, and Olga Rus- sakovsky. 2022. Towards Intersectionality in Ma- chine Learning: Including More Identities, Handling Underrepresentation, and Performing Evaluation. In 2022 ACM Conference on Fairness Accountability and Transparency, pages 336–349. Ryan Whetten, Titouan Parcollet, Marco Dinarelli, and Y annick Estèv...

    work page internal anchor Pith review Pith/arXiv arXiv 2022

  7. [7]

    We like- wise repeat out KNN distance analyses on pre- trained S3Ms in Figure 13

    We note the same patterns in pretrained mod- els as their ASR-finetuned complements. We like- wise repeat out KNN distance analyses on pre- trained S3Ms in Figure 13. (We excluded Wav2vec 2.0 models to avoid visual contamination by their strange behaving final several layers ( Pasad et al. , 2022)). Note that pretrained models exhibit the same patterns of i...

    work page 2022