  Automatic speech recognition (ASR) systems, increasingly prevalent in
education, healthcare, employment, and mobile technology, face significant
challenges in inclusivity, particularly for the 80 million-strong global
community of people who stutter. These systems often fail to accurately
interpret speech patterns deviating from typical fluency, leading to critical
usability issues and misinterpretations. This study evaluates six leading ASRs,
analyzing their performance on both a real-world dataset of speech samples from
individuals who stutter and a synthetic dataset derived from the widely-used
LibriSpeech benchmark. The synthetic dataset, uniquely designed to incorporate
various stuttering events, enables an in-depth analysis of each ASR's handling
of disfluent speech. Our comprehensive assessment includes metrics such as word
error rate (WER), character error rate (CER), and semantic accuracy of the
transcripts. The results reveal a consistent and statistically significant
accuracy bias across all ASRs against disfluent speech, manifesting in
significant syntactical and semantic inaccuracies in transcriptions. These
findings highlight a critical gap in current ASR technologies, underscoring the
need for effective bias mitigation strategies. Addressing this bias is
imperative not only to improve the technology's usability for people who
stutter but also to ensure their equitable and inclusive participation in the
rapidly evolving digital landscape.
