  Automatic speech recognition (ASR) systems often falter while processing
stuttering-related disfluencies -- such as involuntary blocks and word
repetitions -- yielding inaccurate transcripts. A critical barrier to progress
is the scarcity of large, annotated disfluent speech datasets. Therefore, we
present an inclusive ASR design approach, leveraging large-scale
self-supervised learning on standard speech followed by targeted fine-tuning
and data augmentation on a smaller, curated dataset of disfluent speech. Our
data augmentation technique enriches training datasets with various
disfluencies, enhancing ASR processing of these speech patterns. Results show
that fine-tuning wav2vec 2.0 with even a relatively small, labeled dataset,
alongside data augmentation, can significantly reduce word error rates for
disfluent speech. Our approach not only advances ASR inclusivity for people who
stutter, but also paves the way for ASRs that can accommodate wider speech
variations.
