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SEP-28k: A Dataset for Stuttering Event Detection from Podcasts with People Who Stutter
Open accessAbstract
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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“Similar limitations are common in accessibility datasets, such as sign languages [12, 27] and stuttered speech [58], which rarely involve affected communities in the annotation process.”
From Disability-First AI Dataset Annotation: Co-designing Stuttered Speech Annotation Guidelines with People Who Stutter (2026) · cites this paper as [58]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In contrast to conventional annotation guidelines that focus primarily on audible speech [58, 83]”
From Disability-First AI Dataset Annotation: Co-designing Stuttered Speech Annotation Guidelines with People Who Stutter (2026) · cites this paper as [58]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“However, access to these datasets is often restricted, as they typically involve sensitive or proprietary data, except when sourced from public domains such as podcasts [58].”
From Disability-First AI Dataset Annotation: Co-designing Stuttered Speech Annotation Guidelines with People Who Stutter (2026) · cites this paper as [58]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Sep-28k consists of over 28k three-second audio clips curated from public podcasts featuring PWS; however, the annotators' backgrounds remain unclear.”
From Disability-First AI Dataset Annotation: Co-designing Stuttered Speech Annotation Guidelines with People Who Stutter (2026) · cites this paper as [58]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“as the largest publicly available English stuttering dataset, Sep-28k does not have transcriptions and provides only clip-level annotations rather than word-/syllablelevel labels [58].”
From Disability-First AI Dataset Annotation: Co-designing Stuttered Speech Annotation Guidelines with People Who Stutter (2026) · cites this paper as [58]
Experimental — accuracy not yet measured
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“We focused on Sep-28k due to its widespread use 4 , its public availability of annotations produced by PWS [92] and non-PWS [58], and the reported discrepancy between these two versions [92].”
From Disability-First AI Dataset Annotation: Co-designing Stuttered Speech Annotation Guidelines with People Who Stutter (2026) · cites this paper as [58]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“We adopted the five stuttering events used in Sep-28k as our initial ontology, as these events represent the core disfluency types identified across prior work [32, 58, 63, 83].”
From Disability-First AI Dataset Annotation: Co-designing Stuttered Speech Annotation Guidelines with People Who Stutter (2026) · cites this paper as [58]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Sep-28k reported an inter-rater agreement of 0.11 for identifying speech prolongations, 0.25 for blocks, and 0.39 for determining whether a clip contains disfluencies [58].”
From Disability-First AI Dataset Annotation: Co-designing Stuttered Speech Annotation Guidelines with People Who Stutter (2026) · cites this paper as [58]
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“Similar label inconsistencies have been observed in datasets of sign languages [12] and stuttered speech [58, 92].”
From Disability-First AI Dataset Annotation: Co-designing Stuttered Speech Annotation Guidelines with People Who Stutter (2026) · cites this paper as [58]
The cited source says what the claim attributes to it.
In the citing paper
“As Sep-28k divides audio into strictly 3-second clips [58], it is often difficult to determine stuttering events without access to the conversational flow and context.”
From Disability-First AI Dataset Annotation: Co-designing Stuttered Speech Annotation Guidelines with People Who Stutter (2026) · cites this paper as [58]
The cited source says what the claim attributes to it.
In the citing paper
“Our guidelines differ from SLP frameworks developed to measure fluency and stuttering severity [83, 109], as well as from earlier labeling frameworks designed primarily to detect stuttering events [58].”
From Disability-First AI Dataset Annotation: Co-designing Stuttered Speech Annotation Guidelines with People Who Stutter (2026) · cites this paper as [58]
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“people who stutter (PWS) are rarely involved to address such complexities [6, 58, 83, 102].”
From Disability-First AI Dataset Annotation: Co-designing Stuttered Speech Annotation Guidelines with People Who Stutter (2026) · cites this paper as [58]
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Metadata
- Authors
- Lea, C., Mitra, V., Joshi, A., Kajarekar, S., & Bigham, J. P.
- Affiliations
- Apple (United States) (US)
- Venue
- ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Publisher
- IEEE
- Published
- 2021-06-06
- Pages
- 6798-6802
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W3132874366
- Cited by
- 113
- References
- 23
- Citations by year
- 2026: 13 · 2025: 29 · 2024: 33 · 2023: 21 · 2022: 15 · 2021: 2
- FWCI
- 140.62
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE International Conference on Acoustics Speech and Signal Processing (publishedVersion)
- arXiv (Cornell University) (submittedVersion, public-domain)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- IEEE RESOURCE CENTERS
- Topics
- Social Sciences › Psychology › Clinical Psychology › Stuttering Research and Treatment
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Keywords
- stuttering, podcasts, speech annotation, prolongation, sound repetition, word repetition, interjections, training data size
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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