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基于集群的对比对比损失对于噪音-强大的语音识别

Geon Woo Lee1, Hong Kook Kim1,2,3

  • 1AI Graduate School, Gwangju Institute of Science and Technology, Gwangju 61005, Republic of Korea.

Sensors (Basel, Switzerland)
|April 27, 2024
PubMed
概括

本研究介绍了一种用于语音增强 (SE) 和自动语音识别 (ASR) 的联合训练方法,使用一种新的基于集群的对对比 (CBPC) 损失. 这种方法提高了语音质量,并降低了在噪音条件下文字错误率 (WER).

关键词:
声学标记器 (Acoustic Tokenizer) 是一个声学标记器.相反的损失损失.联合培训 联合培训 联合培训噪音强大的语音识别系统自主监督学习学习语音增强器 语音增强器

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