# SEP-28k: A Dataset for Stuttering Event Detection From Podcasts With People Who Stutter

arXiv: https://arxiv.org/abs/2102.12394 · HTML: https://mlsanity.com/paper/2102.12394/

## Key facts

- Cited by 52 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Feb 2021.

## Built on by (most cited first, 30 of 52)

- [Detecting Dysfluencies in Stuttering Therapy Using wav2vec 2.0](https://mlsanity.com/paper/2204.03417/) (2022)
- [KSoF: The Kassel State of Fluency Dataset -- A Therapy Centered Dataset of Stuttering](https://mlsanity.com/paper/2203.05383/) (2022)
- [Machine Learning for Stuttering Identification: Review, Challenges and Future Directions](https://mlsanity.com/paper/2107.04057/) (2021)
- [YOLO-Stutter: End-to-end Region-Wise Speech Dysfluency Detection](https://mlsanity.com/paper/2408.15297/) (2024)
- [Lost in Transcription: Identifying and Quantifying the Accuracy Biases of Automatic Speech Recognition Systems Against Disfluent Speech](https://mlsanity.com/paper/2405.06150/) (2024)
- [Analysis and Tuning of a Voice Assistant System for Dysfluent Speech](https://mlsanity.com/paper/2106.11759/) (2021)
- [Enhancing ASR for Stuttered Speech with Limited Data Using Detect and Pass](https://mlsanity.com/paper/2202.05396/) (2022)
- [The Influence of Dataset Partitioning on Dysfluency Detection Systems](https://mlsanity.com/paper/2206.03400/) (2022)
- [A Stutter Seldom Comes Alone -- Cross-Corpus Stuttering Detection as a Multi-label Problem](https://mlsanity.com/paper/2305.19255/) (2023)
- [FairSSD: Understanding Bias in Synthetic Speech Detectors](https://mlsanity.com/paper/2404.10989/) (2024)
- [Large Language Models for Dysfluency Detection in Stuttered Speech](https://mlsanity.com/paper/2406.11025/) (2024)
- [Stutter-Solver: End-to-end Multi-lingual Dysfluency Detection](https://mlsanity.com/paper/2409.09621/) (2024)
- [Analysis and Evaluation of Synthetic Data Generation in Speech Dysfluency Detection](https://mlsanity.com/paper/2505.22029/) (2025)
- [Filler Word Detection and Classification: A Dataset and Benchmark](https://mlsanity.com/paper/2203.15135/) (2022)
- [Robust Stuttering Detection via Multi-task and Adversarial Learning](https://mlsanity.com/paper/2204.01735/) (2022)
- [Automatic Disfluency Detection from Untranscribed Speech](https://mlsanity.com/paper/2311.00867/) (2023)
- [Introducing ECAPA-TDNN and Wav2Vec2.0 Embeddings to Stuttering Detection](https://mlsanity.com/paper/2204.01564/) (2022)
- [End-to-End and Self-Supervised Learning for ComParE 2022 Stuttering Sub-Challenge](https://mlsanity.com/paper/2207.10817/) (2022)
- [Advancing Stuttering Detection via Data Augmentation, Class-Balanced Loss and Multi-Contextual Deep Learning](https://mlsanity.com/paper/2302.11343/) (2023)
- [Whisper in Focus: Enhancing Stuttered Speech Classification with Encoder Layer Optimization](https://mlsanity.com/paper/2311.05203/) (2023)
- [AS-70: A Mandarin stuttered speech dataset for automatic speech recognition and stuttering event detection](https://mlsanity.com/paper/2406.07256/) (2024)
- [Inclusive ASR for Disfluent Speech: Cascaded Large-Scale Self-Supervised Learning with Targeted Fine-Tuning and Data Augmentation](https://mlsanity.com/paper/2406.10177/) (2024)
- [Anonymising Elderly and Pathological Speech: Voice Conversion Using DDSP and Query-by-Example](https://mlsanity.com/paper/2410.15500/) (2024)
- [Stuttering Detection Using Speaker Representations and Self-supervised Contextual Embeddings](https://mlsanity.com/paper/2306.00689/) (2023)
- [Voice Conversion for Stuttered Speech, Instruments, Unseen Languages and Textually Described Voices](https://mlsanity.com/paper/2310.08104/) (2023)
- [Missingness-resilient Video-enhanced Multimodal Disfluency Detection](https://mlsanity.com/paper/2406.06964/) (2024)
- [Self-supervised Speech Models for Word-Level Stuttered Speech Detection](https://mlsanity.com/paper/2409.10704/) (2024)
- [Stutter-TTS: Controlled Synthesis and Improved Recognition of Stuttered Speech](https://mlsanity.com/paper/2211.09731/) (2022)
- [Enhancing Naturalness in LLM-Generated Utterances through Disfluency Insertion](https://mlsanity.com/paper/2412.12710/) (2024)
- [Boli: A dataset for understanding stuttering experience and analyzing stuttered speech](https://mlsanity.com/paper/2501.15877/) (2025)

## Abstract (arXiv)

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.