Unsupervised Rhythm and Voice Conversion to Improve ASR on Dysarthric Speech

• URL: http://arxiv.org/abs/2506.01618v1
• Date: Mon, 02 Jun 2025 12:57:36 GMT
• Title: Unsupervised Rhythm and Voice Conversion to Improve ASR on Dysarthric Speech
• Authors: Karl El Hajal, Enno Hermann, Sevada Hovsepyan, Mathew Magimai. -Doss,
• Abstract summary: We explore dysarthric-to-healthy speech conversion for improved ASR performance.<n>Our approach extends the Rhythm and Voice (RnV) conversion framework by introducing a syllable-based rhythm modeling method.<n>Experiments on the Torgo corpus reveal that LF-MMI achieves significant word error rate reductions.
• Score: 17.105048387175817
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automatic speech recognition (ASR) systems struggle with dysarthric speech due to high inter-speaker variability and slow speaking rates. To address this, we explore dysarthric-to-healthy speech conversion for improved ASR performance. Our approach extends the Rhythm and Voice (RnV) conversion framework by introducing a syllable-based rhythm modeling method suited for dysarthric speech. We assess its impact on ASR by training LF-MMI models and fine-tuning Whisper on converted speech. Experiments on the Torgo corpus reveal that LF-MMI achieves significant word error rate reductions, especially for more severe cases of dysarthria, while fine-tuning Whisper on converted data has minimal effect on its performance. These results highlight the potential of unsupervised rhythm and voice conversion for dysarthric ASR. Code available at: https://github.com/idiap/RnV

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