Researchers Develop Personalized Federated Learning Approach for Dysarthric Speech Recognition
Researchers have proposed two personalized federated learning strategies to improve automatic speech recognition (ASR) for people with dysarthria, a motor speech disorder. The study addresses a key limitation of standard federated learning — that forcing all users to share identical model components performs poorly when speaker variability is high. Experiments on two benchmark datasets showed statistically significant reductions in word error rate compared to a standard federated learning baseline.
A new preprint posted to arXiv presents personalized federated learning (FL) methods designed to improve ASR accuracy for dysarthric speakers, who face significant barriers with standard speech recognition systems. Federated learning is attractive in this context because it allows model training across distributed devices without sharing raw audio data, preserving user privacy. However, conventional FL approaches like FedAvg struggle with the high degree of speaker variability inherent in dysarthric speech. The researchers explored two aggregation strategies — a parameter-based averaging approach and an embedding-based averaging approach — to introduce personalization into the federated framework. Evaluated on the UASpeech and TORGO dysarthric speech datasets, the proposed methods achieved word error rate reductions of up to 0.99 percentage points absolute (3.15% relative) on UASpeech and 0.56 percentage points absolute (4.73% relative) on TORGO over a regularized FedAvg baseline. Both improvements were reported as statistically significant. The work represents an early step in a relatively underexplored area, as personalized FL research specifically targeting dysarthric speech remains limited.
What's missing
It is unclear how the methods would scale to larger, more diverse dysarthric speaker populations beyond the two benchmark datasets used. The paper has not yet undergone peer review.
What different sources said
- arXiv cs.AICenter
Towards Personalized Federated Learning for Dysarthric Speech Recognition
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