# KSoF: The Kassel State of Fluency Dataset -- A Therapy Centered Dataset of Stuttering

arXiv: https://arxiv.org/abs/2203.05383 · HTML: https://mlsanity.com/paper/2203.05383/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 30 references that are in the map.
- First posted on arXiv 10 Mar 2022.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Emotion Recognition from Speech Using Wav2vec 2.0 Embeddings](https://mlsanity.com/paper/2104.03502/) (2021)
- [Unsupervised Speech Recognition](https://mlsanity.com/paper/2105.11084/) (2021)
- [SEP-28k: A Dataset for Stuttering Event Detection From Podcasts With People Who Stutter](https://mlsanity.com/paper/2102.12394/) (2021)
- [The INTERSPEECH 2021 Computational Paralinguistics Challenge: COVID-19 Cough, COVID-19 Speech, Escalation & Primates](https://mlsanity.com/paper/2102.13468/) (2021)
- [Detecting Multiple Speech Disfluencies using a Deep Residual Network with Bidirectional Long Short-Term Memory](https://mlsanity.com/paper/1910.12590/) (2019)
- [Towards Automated Assessment of Stuttering and Stuttering Therapy](https://mlsanity.com/paper/2006.09222/) (2020)
- [Identification of primary and collateral tracks in stuttered speech](https://mlsanity.com/paper/2003.01018/) (2020)

## Built on by (most cited first, 21 of 21)

- [Detecting Dysfluencies in Stuttering Therapy Using wav2vec 2.0](https://mlsanity.com/paper/2204.03417/) (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)
- [Large Language Models for Dysfluency Detection in Stuttered Speech](https://mlsanity.com/paper/2406.11025/) (2024)
- [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)
- [The ACM Multimedia 2022 Computational Paralinguistics Challenge: Vocalisations, Stuttering, Activity, & Mosquitoes](https://mlsanity.com/paper/2205.06799/) (2022)
- [Self-supervised Speech Models for Word-Level Stuttered Speech Detection](https://mlsanity.com/paper/2409.10704/) (2024)
- [Boli: A dataset for understanding stuttering experience and analyzing stuttered speech](https://mlsanity.com/paper/2501.15877/) (2025)
- [Clinical Annotations for Automatic Stuttering Severity Assessment](https://mlsanity.com/paper/2506.00644/) (2025)
- [ParlaSpeech 3.0: Richly Annotated Spoken Parliamentary Corpora of Croatian, Czech, Polish, and Serbian](https://mlsanity.com/paper/2511.01619/) (2025)
- [Umm... With Transformers? Insights from Filled Pause Use across Four Slavic Parliaments](https://mlsanity.com/paper/2607.05964/) (2026)
- [Detecting Vocal Fatigue with Neural Embeddings](https://mlsanity.com/paper/2204.03428/) (2022)
- [A Comprehensive Rubric for Annotating Pathological Speech](https://mlsanity.com/paper/2404.18851/) (2024)
- [Optimizing Multi-Stuttered Speech Classification: Leveraging Whisper's Encoder for Efficient Parameter Reduction in Automated Assessment](https://mlsanity.com/paper/2406.05784/) (2024)
- [On the Difficulty of Token-Level Modeling of Dysfluency and Fluency Shaping Artifacts](https://mlsanity.com/paper/2512.02027/) (2025)
- [StutterFuse: Mitigating Modality Collapse in Stuttering Detection with Jaccard-Weighted Metric Learning and Gated Fusion](https://mlsanity.com/paper/2512.13632/) (2025)
- [Multilingual Stutter Event Detection for English, German, and Mandarin Speech](https://mlsanity.com/paper/2603.26939/) (2026)
- [Aligning Stuttered-Speech Research with End-User Needs: Scoping Review, Survey, and Guidelines](https://mlsanity.com/paper/2604.20535/) (2026)
- [Cripping AI: Reimagining AI Through Lived Disability Experiences](https://mlsanity.com/paper/2605.02080/) (2026)
- [SpeechDx: A Multi-Task Benchmark for Clinical Speech AI](https://mlsanity.com/paper/2606.17339/) (2026)

## Abstract (arXiv)

Stuttering is a complex speech disorder that negatively affects an individual's ability to communicate effectively. Persons who stutter (PWS) often suffer considerably under the condition and seek help through therapy. Fluency shaping is a therapy approach where PWSs learn to modify their speech to help them to overcome their stutter. Mastering such speech techniques takes time and practice, even after therapy. Shortly after therapy, success is evaluated highly, but relapse rates are high. To be able to monitor speech behavior over a long time, the ability to detect stuttering events and modifications in speech could help PWSs and speech pathologists to track the level of fluency. Monitoring could create the ability to intervene early by detecting lapses in fluency. To the best of our knowledge, no public dataset is available that contains speech from people who underwent stuttering therapy that changed the style of speaking. This work introduces the Kassel State of Fluency (KSoF), a therapy-based dataset containing over 5500 clips of PWSs. The clips were labeled with six stuttering-related event types: blocks, prolongations, sound repetitions, word repetitions, interjections, and - specific to therapy - speech modifications. The audio was recorded during therapy sessions at the Institut der Kasseler Stottertherapie. The data will be made available for research purposes upon request.