arXiv:2606.19823v1 Announce Type: cross 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 (
The continuous improvement in zero-shot voice cloning technology, as exemplified by Higgs Audio V2, is enabling practical applications like enhancing ASR for challenging speech patterns.
This development significantly lowers the barrier for creating robust AI models for underserved populations, opening new markets and improving accessibility for individuals with speech impediments.
The reliance on extensive, speaker-specific data collection for training specialized ASR models is reduced, allowing for more rapid and scalable deployment of assistive technologies.
- · ASR developers
- · Individuals with dysarthria
- · Assistive technology sector
- · Voice cloning companies
- · Traditional data collection services for specialized ASR
Improved accuracy and accessibility of speech recognition for dysarthric individuals.
Accelerated development and adoption of AI-powered communication tools for diverse speech patterns.
New commercial opportunities in personalized AI communication assistance for various niche markets.
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Read at arXiv cs.LG