面向自动语音识别（ASR）的构音障碍语音到健康语音的无监督节奏与音色转换
Unsupervised Rhythm and Voice Conversion of Dysarthric to Healthy Speech for ASR
AI总结：
针对ASR系统处理构音障碍语音效果差且现有语速调整方法依赖转录数据的问题，提出结合自监督表示的无监督节奏与音色转换方法，可显著提升重度构音障碍说话者的ASR性能。
摘要：
众所周知，自动语音识别（ASR）系统在处理构音障碍语音时表现不佳。以往的研究通过调整语速来减少其与典型语音的不匹配，以解决这一问题。但遗憾的是，这些方法依赖带转录的语音数据来估计语速和音素时长，而这类数据对于未见过的说话者可能无法获取。因此，我们结合基于自监督语音表示的无监督节奏与音色转换方法，将构音障碍语音映射为典型语音。我们用一个在健康语音上预训练且未经过进一步微调的大型ASR模型对输出结果进行评估，发现所提出的节奏转换方法尤其能提升Torgo语料库中构音障碍更严重的说话者的识别性能。代码和音频样本可在https://idiap.github.io/RnV 获取。
英文摘要：
Automatic speech recognition (ASR) systems are well known to perform poorly on dysarthric speech. Previous works have addressed this by speaking rate modification to reduce the mismatch with typical speech. Unfortunately, these approaches rely on transcribed speech data to estimate speaking rates and phoneme durations, which might not be available for unseen speakers. Therefore, we combine unsupervised rhythm and voice conversion methods based on self-supervised speech representations to map dysarthric to typical speech. We evaluate the outputs with a large ASR model pre-trained on healthy speech without further fine-tuning and find that the proposed rhythm conversion especially improves performance for speakers of the Torgo corpus with more severe cases of dysarthria. Code and audio samples are available at https://idiap.github.io/RnV .
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
