Adapting Foundation ASR Models to Dysarthric Speech: A Case Study

Christian Huber, Laura Kernahan, Alexander Waibel
6/30/2026
cs.CL

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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.

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Cite this paper

@article{huber2026adapting,
  title  = {Adapting Foundation ASR Models to Dysarthric Speech: A Case Study},
  author = {Christian Huber and Laura Kernahan and Alexander Waibel},
  year   = {2026},
  eprint = {2606.31722},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2606.31722v1}
}

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