🤖 AI Summary
Dysarthric speech recognition faces significant challenges, including substantial inter-speaker variability, pronounced acoustic-phonetic deviations from healthy speech, and scarcity of speaker-specific annotated data—leading to overfitting. To address these issues, this paper proposes a cross-speaker joint fine-tuning strategy based on a pre-trained automatic speech recognition (ASR) model, simultaneously fine-tuning on dysarthric speech from multiple speakers in the CDSD corpus. Unlike conventional speaker-isolated fine-tuning paradigms, our approach leverages shared representation learning across pathological speech to enhance generalization to diverse articulatory impairments, thereby substantially reducing reliance on per-speaker labeled data. Experimental results demonstrate that the proposed method achieves up to a 13.15% absolute reduction in word error rate (WER) on target speakers compared to speaker-specific fine-tuning, with marked improvements in recognition accuracy. This work provides an efficient and practical solution for low-resource dysarthric ASR.
📝 Abstract
Dysarthric speech recognition faces challenges from severity variations and disparities relative to normal speech. Conventional approaches individually fine-tune ASR models pre-trained on normal speech per patient to prevent feature conflicts. Counter-intuitively, experiments reveal that multi-speaker fine-tuning (simultaneously on multiple dysarthric speakers) improves recognition of individual speech patterns. This strategy enhances generalization via broader pathological feature learning, mitigates speaker-specific overfitting, reduces per-patient data dependence, and improves target-speaker accuracy - achieving up to 13.15% lower WER versus single-speaker fine-tuning.