Adapting Foundation ASR Models to Dysarthric Speech: A Case Study
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Computer Science > Computation and Language
Title:Adapting Foundation ASR Models to Dysarthric Speech: A Case Study
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.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2606.31722 [cs.CL] |
| (or arXiv:2606.31722v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.31722
arXiv-issued DOI via DataCite (pending registration)
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