DARS: Dysarthria-Aware Rhythm-Style Synthesis for ASR Enhancement
Dysarthric speech exhibits abnormal prosody and significant speaker variability, presenting persistent challenges for automatic speech recognition (ASR). While text-to-speech (TTS)-based data augmentation has shown potential, existing methods often fail to accurately model the pathological rhythm and acoustic style of dysarthric speech. To address this, we propose DARS, a dysarthria-aware rhythm-style synthesis framework based on the Matcha-TTS architecture. DARS incorporates a multi-stage rhythm predictor optimized by contrastive preferences between normal and dysarthric speech, along with a dysarthric-style conditional flow matching mechanism, jointly enhancing temporal rhythm reconstru...
| Authors | Minghui Wu, Xueling Liu, Jiahuan Fan et al. |
|---|---|
| Primary category | cs.SD |
| Published | 2 March 2026 |
| Last updated at source | 2 March 2026 |
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