Low-Burden Data Augmentation for Dysarthric ASR via Zero-Shot Voice Cloning

Satwinder Singh; Qianli Wang; Zihan Zhong; Clarion Mendes; Hasegawa-Johnson; Waleed Abdulla; Seyed Reza Shahamiri

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arXiv · 2606.19823

Low-Burden Data Augmentation for Dysarthric ASR via Zero-Shot Voice Cloning

Satwinder Singh·Qianli Wang·Zihan Zhong·Clarion Mendes·Hasegawa-Johnson·Waleed Abdulla·Seyed Reza Shahamiri

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Abstract

Automatic speech recognition remains unreliable for dysarthric speech due to data scarcity and high inter-speaker variability. While synthetic data can address these gaps, traditional methods often require extensive speaker-specific data, reintroducing the collection bottleneck. We investigate zero-shot voice cloning as a low-burden augmentation strategy, using Higgs Audio V2 to clone speakers in the TORGO dataset. We fine-tune (FT) Whisper-medium on cloned, real, and hybrid data and evaluate on held-out real speech. Compared to the zero-shot (31.62%), Clone FT achieved a competitive 26.00% WER, nearly matching the 24.44% and 25.12% seen with Real and Hybrid FT, respectively. Notably, Clone and Hybrid FT outperform Real FT for moderate-severe speakers. Clone FT achieves the best results (11.45% relative) in cross-corpus evaluation on the SAP-1102. These results suggest that zero-shot cloning provides scalable training data that circumvents the costly data collection bottleneck.

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BibTeX RIS

Satwinder Singh, Qianli Wang, Zihan Zhong, Clarion Mendes, Hasegawa-Johnson, Waleed Abdulla, Seyed Reza Shahamiri. 2026-06-18. Low-Burden Data Augmentation for Dysarthric ASR via Zero-Shot Voice Cloning. https://arxiv.org/abs/2606.19823

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