Contrastive Regularization for Accent-Robust ASR
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 promote...
| Authors | Van-Phat Thai, Aradhya Dhruv, Duc-Thinh Pham et al. |
|---|---|
| Primary category | cs.SD |
| Published | 5 May 2026 |
| Last updated at source | 14 June 2026 |
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