  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.
