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arXiv · 2508.06391

Improved Dysarthric Speech to Text Conversion via TTS Personalization

Péter Mihajlik·Éva Székely·Piroska Barta·Máté Soma Kádár·Gergely Dobsinszki·László Tóth
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Abstract

We present a case study on developing a customized speech-to-text system for a Hungarian speaker with severe dysarthria. State-of-the-art automatic speech recognition (ASR) models struggle with zero-shot transcription of dysarthric speech, yielding high error rates. To improve performance with limited real dysarthric data, we fine-tune an ASR model using synthetic speech generated via a personalized text-to-speech (TTS) system. We introduce a method for generating synthetic dysarthric speech with controlled severity by leveraging premorbidity recordings of the given speaker and speaker embedding interpolation, enabling ASR fine-tuning on a continuum of impairments. Fine-tuning on both real and synthetic dysarthric speech reduces the character error rate (CER) from 36-51% (zero-shot) to 7.3%. Our monolingual FastConformer_Hu ASR model significantly outperforms Whisper-turbo when fine-tuned on the same data, and the inclusion of synthetic speech contributes to an 18% relative CER reduction. These results highlight the potential of personalized ASR systems for improving accessibility for individuals with severe speech impairments.

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Péter Mihajlik, Éva Székely, Piroska Barta, Máté Soma Kádár, Gergely Dobsinszki, László Tóth. 2025-08-08. Improved Dysarthric Speech to Text Conversion via TTS Personalization. https://arxiv.org/abs/2508.06391

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