Regularized Federated Learning for Privacy-Preserving Dysarthric and Elderly Speech Recognition
This work addresses privacy-preserving speech recognition for dysarthric and elderly individuals, representing an incremental improvement in federated learning methods for a specific domain.
The paper tackled the challenge of accurate dysarthric and elderly speech recognition by investigating regularized federated learning techniques to address data scarcity and privacy concerns, achieving statistically significant word error rate reductions of up to 0.55% absolute (2.13% relative) compared to baseline systems.
Accurate recognition of dysarthric and elderly speech remains challenging to date. While privacy concerns have driven a shift from centralized approaches to federated learning (FL) to ensure data confidentiality, this further exacerbates the challenges of data scarcity, imbalanced data distribution and speaker heterogeneity. To this end, this paper conducts a systematic investigation of regularized FL techniques for privacy-preserving dysarthric and elderly speech recognition, addressing different levels of the FL process by 1) parameter-based, 2) embedding-based and 3) novel loss-based regularization. Experiments on the benchmark UASpeech dysarthric and DementiaBank Pitt elderly speech corpora suggest that regularized FL systems consistently outperform the baseline FedAvg system by statistically significant WER reductions of up to 0.55\% absolute (2.13\% relative). Further increasing communication frequency to one exchange per batch approaches centralized training performance.