Cross-Learning Fine-Tuning Strategy for Dysarthric Speech Recognition Via CDSD database

• URL: http://arxiv.org/abs/2508.18732v1
• Date: Tue, 26 Aug 2025 07:00:12 GMT
• Title: Cross-Learning Fine-Tuning Strategy for Dysarthric Speech Recognition Via CDSD database
• Authors: Qing Xiao, Yingshan Peng, PeiPei Zhang,
• Abstract summary: Dysarthric speech recognition faces challenges from severity variations and disparities relative to normal speech.<n> Conventional approaches individually fine-tune ASR models pre-trained on normal speech per patient to prevent feature conflicts.<n>Experiments reveal that multi-speaker fine-tuning (simultaneously on multiple dysarthric speakers) improves recognition of individual speech patterns.
• Score: 7.78293690567929
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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