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arxiv: 2605.03297 · v1 · submitted 2026-05-05 · 💻 cs.SD · cs.LG

Recognition: unknown

Contrastive Regularization for Accent-Robust ASR

Van-Phat Thai , Aradhya Dhruv , Duc-Thinh Pham , Sameer Alam
Authors on Pith no claims yet

Pith reviewed 2026-05-07 13:28 UTC · model grok-4.3

classification 💻 cs.SD cs.LG
keywords accent robustnessautomatic speech recognitioncontrastive learningself-supervised pretrainingCTC fine-tuningrepresentation regularizationL2-ARCTIC
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The pith

Utterance-level contrastive loss regularizes ASR encoders to reduce accent sensitivity without model changes or accent labels.

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

The paper examines supervised contrastive learning as an auxiliary training objective for automatic speech recognition systems that already use self-supervised pretraining and CTC fine-tuning. By adding an utterance-level contrastive loss, the approach pulls representations of utterances sharing the same transcript closer together while pushing apart those with different transcripts, creating greater invariance to accent-related pronunciation differences. This regularization requires no architectural alterations to the encoder or decoder and operates without any explicit accent annotations during training. Experiments across several pretrained models on the L2-ARCTIC benchmark demonstrate lower word error rates, with the largest gains appearing when the model encounters accents absent from the fine-tuning data. Representation analysis further shows that the loss produces tighter clusters of embeddings for the same transcript even when accent varies.

Core claim

Supervised contrastive learning (SupCon) serves as a lightweight, accent-invariant auxiliary objective for CTC fine-tuning. An utterance-level contrastive loss regularizes encoder representations without architectural modification or explicit accent supervision. Experiments on the L2-ARCTIC benchmark show consistent WER reductions across multiple pretrained encoders, with up to 25-29% relative reduction under unseen-accent evaluation. Analysis using within-transcript cosine dispersion indicates that SupCon promotes more compact and stable representation geometry under accent variability.

What carries the argument

Utterance-level supervised contrastive loss (SupCon) applied during CTC fine-tuning to pull same-transcript encoder representations together and thereby increase accent invariance.

If this is right

  • Consistent word error rate reductions occur across multiple pretrained encoders when the contrastive objective is added to standard CTC fine-tuning.
  • Relative WER improvements reach 25-29% specifically on accents held out from training.
  • Within-transcript cosine dispersion decreases, indicating more compact representation clusters despite accent variation.
  • The method applies without any change to model architecture or need for accent labels.
  • It functions as a model-agnostic regularization step that can be inserted into existing self-supervised pretraining plus CTC pipelines.

Where Pith is reading between the lines

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

  • The same regularization might also improve robustness to other uncontrolled factors such as background noise or speaking rate, since the loss operates on transcript identity rather than accent identity.
  • Combining the contrastive term with existing data-augmentation strategies could produce additive gains on more challenging test distributions.
  • Testing the approach on larger, multi-accent corpora would reveal whether the observed geometry changes scale with data diversity.
  • Deployment in production systems serving international users could benefit from this lightweight addition if the WER gains hold on real-world traffic.

Load-bearing premise

That the within-transcript cosine dispersion metric reliably indicates accent invariance and that gains on the L2-ARCTIC benchmark generalize beyond the tested accents and models.

What would settle it

No measurable drop in word error rate or within-transcript cosine dispersion when the contrastive loss is added to a previously unseen set of accents or to a different family of pretrained encoders would falsify the claimed robustness benefit.

Figures

Figures reproduced from arXiv: 2605.03297 by Aradhya Dhruv, Duc-Thinh Pham, Sameer Alam, Van-Phat Thai.

Figure 1
Figure 1. Figure 1: Overview of the proposed training model. A self-supervised acoustic encoder is trained with a primary CTC objective for ASR, while an auxiliary supervised contrastive loss is applied to utterance-level representations obtained via masked pooling of encoder hidden states. The auxiliary module is used only during training and does not affect inference. B, T, D, V , and P denote batch size, number of encoder … view at source ↗
Figure 2
Figure 2. Figure 2: illustrates this effect using t-SNE on a shared sub￾set of transcripts. Compared to W2V2-Large (CTC), the Sup￾Con model produces more compact transcript-level clusters, suggesting improved invariance to speaker and accent variation. Quantitatively, SupCon consistently reduces within-transcript view at source ↗
read the original abstract

ASR systems based on self-supervised acoustic pretraining and CTC fine-tuning achieve strong performance on native speech but remain sensitive to accent variability. We investigate supervised contrastive learning (SupCon) as a lightweight, accent-invariant auxiliary objective for CTC fine-tuning. An utterance-level contrastive loss regularizes encoder representations without architectural modification or explicit accent supervision. Experiments on the L2-ARCTIC benchmark show consistent WER reductions across multiple pretrained encoders, with up to 25 -- 29\% relative reduction under unseen-accent evaluation. Analysis using within-transcript cosine dispersion indicates that SupCon promotes more compact and stable representation geometry under accent variability. Overall, SupCon provides an effective and model-agnostic regularization strategy for improving accent robustness.

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 claims that an utterance-level supervised contrastive (SupCon) loss can be used as a lightweight auxiliary objective during CTC fine-tuning of pretrained ASR encoders to regularize representations for improved accent robustness, without architectural changes or explicit accent labels. On the L2-ARCTIC benchmark, this yields consistent WER reductions across multiple encoders, with relative gains up to 25-29% in unseen-accent evaluation settings. The authors further support the approach with an analysis showing reduced within-transcript cosine dispersion, indicating more compact representation geometry under accent variability.

Significance. If the empirical gains and mechanistic interpretation hold, the work provides a practical, model-agnostic regularization strategy for accent-robust ASR that requires no additional supervision. The consistent improvements across several pretrained encoders is a positive aspect that strengthens the case for broad applicability. However, the significance is tempered by the need for stronger validation that the gains reflect accent invariance rather than benchmark-specific effects.

major comments (2)
  1. [Analysis] Analysis section: The within-transcript cosine dispersion metric is computed using transcript identity to define groups, which is the identical grouping used to select positive pairs in the SupCon loss. This makes the metric confirmatory of the loss's direct objective rather than independent evidence that representations have become invariant to accent (as opposed to simply more compact within transcripts).
  2. [Experiments] Experiments section: The reported WER reductions lack accompanying details on statistical significance testing, exact hyperparameter values for the contrastive loss weighting coefficient, baseline configurations, and the precise unseen-accent data splits on L2-ARCTIC. These omissions make it difficult to assess whether the up to 25-29% relative gains are robust or reproducible.
minor comments (1)
  1. [Abstract] Abstract: The phrasing '25 -- 29% relative reduction' would benefit from a brief qualifier indicating the specific models or conditions under which the upper end is achieved.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our work. We address each major comment below and have made revisions to improve the manuscript's clarity, rigor, and reproducibility.

read point-by-point responses

  1. Referee: [Analysis] Analysis section: The within-transcript cosine dispersion metric is computed using transcript identity to define groups, which is the identical grouping used to select positive pairs in the SupCon loss. This makes the metric confirmatory of the loss's direct objective rather than independent evidence that representations have become invariant to accent (as opposed to simply more compact within transcripts).

    Authors: We agree that the within-transcript cosine dispersion metric relies on the same transcript-based grouping as the SupCon loss, rendering it confirmatory of the loss objective rather than fully independent validation of accent invariance. The metric was included to demonstrate the geometric impact of regularization on content-matched utterances (which vary in accent within L2-ARCTIC). In the revised manuscript, we have updated the analysis section to explicitly acknowledge this relationship, tempered the interpretation to focus on compactness under content-matched conditions, and added a brief discussion of its limitations as standalone evidence for accent invariance. We also include a supplementary figure showing dispersion trends across different groupings to provide additional context. revision: partial

  2. Referee: [Experiments] Experiments section: The reported WER reductions lack accompanying details on statistical significance testing, exact hyperparameter values for the contrastive loss weighting coefficient, baseline configurations, and the precise unseen-accent data splits on L2-ARCTIC. These omissions make it difficult to assess whether the up to 25-29% relative gains are robust or reproducible.

    Authors: We acknowledge these omissions in the original submission. The revised manuscript now incorporates: statistical significance testing via bootstrap resampling with reported p-values for all WER comparisons; the exact contrastive loss weighting coefficient (λ = 0.1); complete baseline configuration details including all pretrained encoders, fine-tuning schedules, and optimization settings; and the precise L2-ARCTIC unseen-accent splits (holding out Arabic, Mandarin, and Spanish speakers for evaluation while training on the remaining accents). These additions enable full reproducibility and allow assessment of the robustness of the reported relative gains. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical application of standard loss with independent evaluation

full rationale

The paper applies the standard supervised contrastive (SupCon) loss as an auxiliary objective during CTC fine-tuning of pretrained ASR encoders, without deriving new equations or claiming first-principles predictions. Reported gains consist of measured WER reductions on the L2-ARCTIC benchmark (including unseen-accent splits) plus post-hoc analysis of within-transcript cosine dispersion. These outcomes are obtained via direct experimentation rather than any reduction of results to fitted parameters, self-citations, or quantities defined by the inputs. The contrastive formulation is a known technique used here in a model-agnostic way; the evaluation metrics and benchmark results remain externally falsifiable and do not collapse to the training objective by construction. No load-bearing self-citation chains or ansatz smuggling appear in the provided claims.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The approach rests on the assumption that contrastive regularization at utterance level produces accent-invariant features; no new entities are introduced and free parameters are limited to standard loss weighting.

free parameters (1)
  • contrastive loss weighting coefficient
    Balance between main CTC loss and auxiliary contrastive term must be chosen; not specified in abstract.
axioms (1)
  • domain assumption Utterance-level representations from pretrained encoders can be made more accent-invariant through contrastive regularization without explicit accent labels.
    Central premise of the auxiliary objective.

pith-pipeline@v0.9.0 · 5423 in / 1142 out tokens · 63238 ms · 2026-05-07T13:28:40.897292+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

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    Introduction Modern ASR systems based on self-supervised acoustic pre- training and CTC fine-tuning achieve strong performance on benchmarks dominated by native speech [1, 2, 3]. However, performance degrades substantially for non-native speech, par- ticularly in low-resource or globally deployed settings, due to systematic pronunciation variability that ...

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    Problem Formulation As illustrated in Figure 1, letD={(x i, yi)}N i=1 denote a train- ing dataset, wherex i is a raw speech waveform andyi is the cor- responding transcript

    Methodology 2.1. Problem Formulation As illustrated in Figure 1, letD={(x i, yi)}N i=1 denote a train- ing dataset, wherex i is a raw speech waveform andyi is the cor- responding transcript. Given an utterancex i, a self-supervised pretrained acoustic encoder produces frame-level representa- tions that are shared by the ASR and auxiliary contrastive ob- j...

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    Datasets We conduct experiments on the L2-ARCTIC [4], a widely used benchmark for non-native and multi-accent ASR

    Experimental Setting 3.1. Datasets We conduct experiments on the L2-ARCTIC [4], a widely used benchmark for non-native and multi-accent ASR. The dataset consists of English speech from non-native speakers across six L1 backgrounds: Arabic, Mandarin, Hindi, Korean, Spanish, and Vietnamese. Each accent group includes four speakers (24 speakers in total), wi...

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    Main Results Table 2 reports word error rate (WER) on the L2-ARCTIC benchmark under unseen-transcript (UT) and unseen-accent (UA) evaluation settings

    Results 4.1. Main Results Table 2 reports word error rate (WER) on the L2-ARCTIC benchmark under unseen-transcript (UT) and unseen-accent (UA) evaluation settings. All results are obtained using iden- tical CTC decoding with a 4-gram language model. The pro- posed supervised contrastive regularization consistently im- proves recognition performance over s...

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    Conclusion This paper demonstrates that supervised contrastive learning is an effective utterance-level regularizer for ASR fine-tuning. Without modifying model architectures or pretraining proce- dures, the proposed approach improves accent robustness, sta- bilizes encoder representations, and yields consistent WER re- ductions across multiple self-super...

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    Acknowledgments This research is supported by the National Research Founda- tion, Singapore, and the Civil Aviation Authority of Singapore, under the Aviation Transformation Programme. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation...

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