mlsanityData to 7 Oct 2026

Towards Personalized Federated Learning for Dysarthric Speech Recognition

arXiv 2606.13253 ↗First posted 11 Jun 2026cs.SD cs.AIcited by 0

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Abstract (arXiv)

Speech recognition is challenging for dysarthric speakers. While federated learning (FL)-based ASR can be an effective tool for protecting privacy, it suffers from heterogeneity issues caused by speaker variability. Forcing all speakers to share the same model components can be suboptimal under such heterogeneity, making personalization a promising direction; however, related research on dysarthric speech remains limited. To this end, this paper explores two aggregation strategies to achieve personalization, including the parameter-based averaging strategy and the embedding-based averaging strategy. Experiments on UASpeech and TORGO show that the proposed methods outperform the baseline regularized FedAvg by statistically significant WER reductions of up to 0.99% absolute (3.15% relative) on UASpeech and 0.56% absolute (4.73% relative) on TORGO, respectively.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 25 of its 43 references that are in the map. First posted on arXiv 11 Jun 2026.

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References · 25

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  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  3. 2021HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Unitscited by 2,301
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  12. 2022Investigation of Data Augmentation Techniques for Disordered Speech Recognitioncited by 27
13 more
  1. 2022Speaker adaptation for Wav2vec2 based dysarthric ASRcited by 21
  2. 2021Federated Acoustic Modeling For Automatic Speech Recognitioncited by 20
  3. 2022Speaker Adaptation Using Spectro-Temporal Deep Features for Dysarthric and Elderly Speech Recognitioncited by 19
  4. 2024Enhancing Pre-trained ASR System Fine-tuning for Dysarthric Speech Recognition using Adversarial Data Augmentationcited by 17
  5. 2022Personalized Adversarial Data Augmentation for Dysarthric and Elderly Speech Recognitioncited by 16
  6. 2021Federated Learning in ASR: Not as Easy as You Thinkcited by 14
  7. 2022Semi-FedSER: Semi-supervised Learning for Speech Emotion Recognition On Federated Learning using Multiview Pseudo-Labelingcited by 12
  8. 2023Federated learning for secure development of AI models for Parkinson's disease detection using speech from different languagescited by 9
  9. 2024Self-supervised ASR Models and Features For Dysarthric and Elderly Speech Recognitioncited by 9
  10. 2023Importance of Smoothness Induced by Optimizers in FL4ASR: Towards Understanding Federated Learning for End-to-End ASRcited by 8
  11. 2024Communication-Efficient Personalized Federated Learning for Speech-to-Text Taskscited by 8
  12. 2025Phone-purity Guided Discrete Tokens for Dysarthric Speech Recognitioncited by 2
  13. 2025Regularized Federated Learning for Privacy-Preserving Dysarthric and Elderly Speech Recognitioncited by 1

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