Training Data Augmentation for Dysarthric Automatic Speech Recognition by Text-to-Dysarthric-Speech Synthesis
<|reference_start|>Training Data Augmentation for Dysarthric Automatic Speech Recognition by Text-to-Dysarthric-Speech Synthesis: Automatic speech recognition (ASR) research has achieved impressive performance in recent years and has significant potential for enabling access for people with dysarthria (PwD) in augmentative and alternative communication (AAC) and home environment systems. However, progress in dysarthric ASR (DASR) has been limited by high variability in dysarthric speech and limited public availability of dysarthric training data. This paper demonstrates that data augmentation using text-to-dysarthic-speech (TTDS) synthesis for finetuning large ASR models is effective for DASR. Specifically, diffusion-based text-to-speech (TTS) models can produce speech samples similar to dysarthric speech that can be used as additional training data for fine-tuning ASR foundation models, in this case Whisper. Results show improved synthesis metrics and ASR performance for the proposed multi-speaker diffusion-based TTDS data augmentation for ASR fine-tuning compared to current DASR baselines.<|reference_end|>
@article{leung2024training,
    title={Training Data Augmentation for Dysarthric Automatic Speech Recognition
  by Text-to-Dysarthric-Speech Synthesis},
    author={Wing-Zin Leung, Mattias Cross, Anton Ragni, Stefan Goetze},
    journal={arXiv preprint arXiv:2406.08568},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.08568},
    primaryClass={cs.SD eess.AS}
}