  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.
