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 and stuttered speech, which rarely involve affected communities in the annotation process.
“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]
The Introduction identifies SEP-28k as a stuttered-speech dataset, supporting its use as an example. Section 2.3 (Annotations) says the annotators were trained and were not clinicians, but does not say whether they were people who stutter. Neither passage establishes that affected communities are rarely involved in annotating such datasets. (LLM, conf=0.79)
Experimental — accuracy not yet measuredConventional annotation guidelines focus primarily on audible speech.
“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]
Section 2.3 (Annotations; page unavailable) describes annotating audio clips and notes that blocks are difficult to assess from audio alone because clinicians may rely on physical signs. This supports an audio-focused annotation example, but the paper does not establish that conventional annotation guidelines generally focus primarily on audible speech. (LLM, conf=0.80)
Experimental — accuracy not yet measuredAccess to stuttered speech datasets is often restricted, as they typically involve sensitive or proprietary data, except when sourced from public domains such as podcasts.
“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]
The Introduction (page not available) says annotations from several earlier dysfluency-dataset efforts had not been released and describes SEP-28k as curated from public podcasts. This supports an availability problem and the podcast example, but the paper does not say that access to stuttered-speech datasets is typically restricted because the data are sensitive or proprietary. (LLM, conf=0.84)
Experimental — accuracy not yet measuredSep-28k consists of over 28k three-second audio clips curated from public podcasts featuring PWS, and the annotators' backgrounds remain unclear.
“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]
Section 2.1 reports 28,177 three-second clips from podcasts, and the Abstract says the audio came from public podcasts largely featuring people who stutter. Section 2.3 does give some information about the annotators: at least three people labeled each clip, received training, and were not clinicians. Their backgrounds are therefore not wholly unclear, although the paper gives no further qualifications. (LLM, conf=0.88)
Experimental — accuracy not yet measuredSep-28k does not have transcriptions and provides only clip-level annotations rather than word-/syllable-level labels.
“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]
Section 2.3 (page not provided) says SEP-28k audio was divided into three-second clips with binary labels, supporting the claim that its annotations are clip-level rather than word- or syllable-level. Our copy does not explicitly say that SEP-28k has no transcriptions, so that part of the claim is not established. (LLM, conf=0.86)
Experimental — accuracy not yet measured
We focused on Sep-28k due to its widespread use, its public availability of annotations produced by PWS and non-PWS, and the reported discrepancy between these two versions.
“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]
The Introduction says SEP-28k’s annotations were released publicly, and §2.3 says at least three trained people annotated each clip. Section 2.3 describes them as non-clinicians but does not say whether they were non-PWS; that distinction is not established for the part attributed to [58]. (LLM, conf=0.85)
Experimental — accuracy not yet measuredThe five stuttering events used in Sep-28k represent the core disfluency types identified across prior work.
“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]
The Introduction identifies the five event types studied in SEP-28k and discusses earlier research on detecting several of them. It does not say that prior work identified these five as the core disfluency types; that characterization goes beyond the source. (LLM, conf=0.84)
Experimental — accuracy not yet measuredSep-28k reported low inter-rater agreement: 0.11 for identifying speech prolongations, 0.25 for blocks, and 0.39 for determining whether a clip contains disfluencies.
“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]
Similar label inconsistencies have been observed in datasets of sign languages and stuttered speech.
“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]
Sep-28k divides audio into strictly 3-second clips, making it often difficult to determine stuttering events without access to conversational flow and context.
“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]
Our guidelines differ from SLP frameworks developed to measure fluency and stuttering severity, as well as from earlier labeling frameworks designed primarily to detect stuttering events.
“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]
People who stutter are rarely involved in addressing the complexities of stuttered speech annotation.
“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]
Section 2.3 (Annotations; page unavailable) explains why stuttered-speech annotation is difficult and says the annotators were trained non-clinicians. It does not say whether they were people who stutter or whether people who stutter are rarely involved in annotation. Searches for their involvement and lived experience found no passage addressing that claim. (LLM, conf=0.75)
Experimental — accuracy not yet measured
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Does Data Balancing Impact Stutter Detection and Classification?
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- 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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