Boli: A dataset for understanding stuttering experience and analyzing stuttered speech
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Abstract (arXiv)
There is a growing need for diverse, high-quality stuttered speech data, particularly in the context of Indian languages. This paper introduces Project Boli, a multi-lingual stuttered speech dataset designed to advance scientific understanding and technology development for individuals who stutter, particularly in India. The dataset constitutes (a) anonymized metadata (gender, age, country, mother tongue) and responses to a questionnaire about how stuttering affects their daily lives, (b) captures both read speech (using the Rainbow Passage) and spontaneous speech (through image description tasks) for each participant and (c) includes detailed annotations of five stutter types: blocks, prolongations, interjections, sound repetitions and word repetitions. We present a comprehensive analysis of the dataset, including the data collection procedure, experience summarization of people who stutter, severity assessment of stuttering events and technical validation of the collected data. The dataset is released as an open access to further speech technology development.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 12 of its 19 references that are in the map. First posted on arXiv 27 Jan 2025.
Builds on
12 of 19 references.
References · 12
- 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 3,972
- 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,853
- 2021SEP-28k: A Dataset for Stuttering Event Detection From Podcasts With People Who Stuttercited by 51
- 2019Detecting Multiple Speech Disfluencies using a Deep Residual Network with Bidirectional Long Short-Term Memorycited by 27
- 2022Detecting Dysfluencies in Stuttering Therapy Using wav2vec 2.0cited by 26
- 2022KSoF: The Kassel State of Fluency Dataset -- A Therapy Centered Dataset of Stutteringcited by 21
- 2023From User Perceptions to Technical Improvement: Enabling People Who Stutter to Better Use Speech Recognitioncited by 18
- 2021Analysis and Tuning of a Voice Assistant System for Dysfluent Speechcited by 12
- 2022Enhancing ASR for Stuttered Speech with Limited Data Using Detect and Passcited by 11
- 2022The Influence of Dataset Partitioning on Dysfluency Detection Systemscited by 9
- 2024AS-70: A Mandarin stuttered speech dataset for automatic speech recognition and stuttering event detectioncited by 5
- 2022Dysfluencies Seldom Come Alone -- Detection as a Multi-Label Problemcited by 2
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Citing papers per year: 2026: 3.
3 papers.