Voice Assistants and Stuttering

Stuttering is a speech disorder involving disruptions in the flow of speech, including sound or syllable repetitions, prolonged sounds, and blocks where sound stops entirely for a moment, and it affects a meaningful share of the population at some point, with many people continuing to stutter into adulthood. Speech interfaces combine automatic speech recognition with an endpoint or voice-activity decision about when a person has finished. A pause during a block or a repeated sound may contribute to an early cutoff or a transcription error in some systems, as the cited user study illustrates; the effect varies by person, device, and task. This is a known and documented gap rather than a hypothetical one. Researchers have published work on stuttered speech. SEP-28k is a research dataset with labeled clips of events such as blocks, prolongations, and repetitions; it was created for studying event detection, not as proof that consumer assistants now handle stuttering reliably. A 2023 study surveyed 61 people who stutter and tested speech recognition with recordings from 91 participants, reporting cutoffs and transcription errors in the systems it evaluated. These findings document a gap in those study conditions, not the performance of every current assistant. Related accessibility efforts, such as Google's Project Euphonia, focus more broadly on speech that differs from typical fluent patterns, including stuttering alongside other speech differences. A common misconception is that stuttering is simply 'talking slowly' and that giving an assistant more time to listen fully solves the problem; in reality, disfluencies can include sounds the recognizer misclassifies as noise or as the wrong word entirely, not just longer pauses, so fixing the experience requires models trained on actual disfluent speech patterns, not just adjusted timeout settings. Progress has been described by researchers as incremental, since disfluency patterns vary significantly between individuals.