English(EN) Contrastive Training with LLM-generated Near-Misses for Robust Code-Switching Speech Recognition

新方法提升语码转换语音识别能力

作者 PulseAugur 编辑部 · [2 个来源] · 2026-06-05 07:24

研究人员开发了一种新的对比训练方法，以改进语码转换的自动语音识别，语码转换是指在单次话语中交替使用不同语言。该方法识别关键的语码转换点，并使用大型语言模型生成合理的“近失”假设。使用这种技术对Whisper-small模型进行微调，在特定的语码转换数据集上显示出超过2%的错误率降低。 AI

影响增强了多语言用户的ASR鲁棒性，可能提高语音界面的可访问性和可用性。

排序理由该集群包含一篇详细介绍改进语音识别新方法的学术论文。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

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Newsworthiness bucket: Research; 该集群包含一篇详细介绍改进语音识别新方法的学术论文。
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arXiv cs.CL TIER_1 English(EN) · Tung X. Nguyen, Hieu Minh Truong, Giang-Son Nguyen, Nhu Vo, Wray Buntine, Dung D. Le · 2026-06-08 04:00

使用 LLM 生成的近失样本进行对比训练以实现鲁棒的语种混合语音识别

arXiv:2606.06985v1 Announce Type: new 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 tra…
arXiv cs.CL TIER_1 English(EN) · Dung D. Le · 2026-06-05 07:24

使用 LLM 生成的近失样本进行对比训练以实现鲁棒的语码转换语音识别

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-…