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Contrastive Training with LLM-generated Near-Misses for Robust Code-Switching Speech Recognition

2026-06-05 07:24:39
Tung X. Nguyen, Hieu Minh Truong, Giang-Son Nguyen, Nhu Vo, Wray Buntine, Dung D. Le
arXiv_CL
               
Abstract

Code-switching (CS), the alternation between multiple languages within a single utterance, remains challenging for Automatic Speech Recognition (ASR). To address this issue, we propose a Point-of-Interest (POI)-aware contrastive training framework that improves recognition at CS-critical regions. We first identify CS spans by adopting POI detection method from literature, then construct acoustically plausible near-miss hypotheses by perturbing POIs in ASR N-best outputs and expanding candidates with a large language model. Hard but plausible negatives are retained through filtering with acoustic, phonemic, and textual constraints. Finally, we fine-tune Whisper-small with LoRA using a POI-weighted cross-entropy anchor objective together with a multi-negative contrastive ranking loss. Experiments on CS-FLEURS (cmn-eng) and ViMedCSS (vie-eng) show consistent reductions of over 2% in both general and CS-aware error rates compared to standard LoRA fine-tuning.

Abstract (translated)

URL

https://arxiv.org/abs/2606.06985

PDF

https://arxiv.org/pdf/2606.06985.pdf

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