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Adapting Foundation ASR Models to Dysarthric Speech: A Case Study

2026-06-30 14:23:49
Christian Huber, Laura Kernahan, Alexander Waibel
arXiv_CL
       
Abstract

Automatic speech recognition (ASR) systems often perform poorly in dysarthric speech, limiting their usefulness to affected speakers in everyday communication. This paper presents a personalized ASR system for a dysarthric speaker, built by adapting a foundation ASR model to speaker-specific data. Using the TEQST tool, we collected 92 hours of read speech and later added 8.8 hours of user corrections gathered through a deployed mobile application. Starting from Whisper, fine-tuning reduced word error rate to 15.8% with only 1.4 hours of adaptation data, reached 10.7% with 22.5 hours, and achieved the best result of 9.7% when using all available data including the corrections. Using LoRA adaptation and/or Qwen3-ASR as foundation model performed worse in this setting. The results show that personalized fine-tuning can make foundation ASR models substantially more effective for dysarthric speech and suitable for practical deployment.

Abstract (translated)

URL

https://arxiv.org/abs/2606.31722

PDF

https://arxiv.org/pdf/2606.31722.pdf

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