The ability to automatically detect stuttering events in speech could help speech pathologists track an individual’s fluency over time or help improve speech recognition systems for people with atypical speech patterns. Despite increasing interest in this area, existing public datasets are too small to build generalizable dysfluency detection systems and lack sufficient annotations. In this work, we introduce Stuttering Events in Podcasts (SEP-28k), a dataset containing over 28k clips labeled with five event types including blocks, prolongations, sound repetitions, word repetitions, and interjections. Audio comes from public podcasts largely consisting of people who stutter interviewing other people who stutter. We benchmark a set of acoustic models on SEP-28k and the public FluencyBank dataset and highlight how simply increasing the amount of training data improves relative detection performance by 28% and 24% F1 on each. Annotations from over 32k clips across both datasets will be publicly released.

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Improved Speech Recognition for People Who Stutter

Speech recognition systems have improved substantially in recent years, leading to widespread adoption across computing platforms. Two common forms of speech interaction are voice assistants (VAs) that listen for spoken commands and respond accordingly, and dictation systems, which act as an alternative to a keyboard by converting the user's open-ended speech to written text for messages, emails, and so on. Speech interaction is especially important for devices with smaller or no screens, such as smart speakers and smart headphones, that support speech interaction. Yet speech presents barriers for many people with communication disabilities such as stuttering, dysarthria, or aphasia.

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From User Perceptions to Technical Improvement: Enabling People Who Stutter to Better Use Speech Recognition

Consumer speech recognition systems do not work as well for many people with speech differences, such as stuttering, relative to the rest of the general population. However, what is not clear is the degree to which these systems do not work, how they can be improved, or how much people want to use them. In this paper, we first address these questions using results from a 61-person survey from people who stutter and find participants want to use…
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