Boli: A dataset for understanding stuttering experience and analyzing stuttered speech
Open accessAbstract
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.
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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 [6]
The Introduction says manual annotation of stuttered speech is laborious because stutter types vary in speaking style and duration. It does not say that people who stutter are rarely involved in annotation or in addressing those difficulties; its discussion of their participation concerns recording speech and completing questionnaires. (LLM, conf=0.76)
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Cites
Robust Speech Recognition via Large-Scale Weak Supervision
Radford, A., Kim, J. W., Xu, T., Brockman, G., McLeavey, C., & Sutskever, I. (2022) · arXiv · doi:10.48550/arxiv.2212.04356 · Cited by 1,953 · see citing works
The University College London Archive of Stuttered Speech (UCLASS)
Howell, P., Davis, S., & Bartrip, J. (2009) · Journal of Speech Language and Hearing Research · doi:10.1044/1092-4388(2009/07-0129 · Cited by 127 · see citing works
Fluency Bank: A new resource for fluency research and practice
Bernstein Ratner, N., & MacWhinney, B. (2018) · Journal of Fluency Disorders · doi:10.1016/j.jfludis.2018.03.002 · Cited by 124 · see citing works
SEP-28k: A Dataset for Stuttering Event Detection from Podcasts with People Who Stutter
Lea, C., Mitra, V., Joshi, A., Kajarekar, S., & Bigham, J. P. (2021) · ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp39728.2021.9413520 · Cited by 113 · see citing works
FluentNet: End-to-End Detection of Stuttered Speech Disfluencies With Deep Learning
Kourkounakis, T., Hajavi, A., & Etemad, A. (2021) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2021.3110146 · Cited by 109 · see citing works
Kourkounakis, T., Hajavi, A., & Etemad, A. (2020) · ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp40776.2020.9053893 · Cited by 87 · see citing works
Disordered speech data collection: lessons learned at 1 million utterances from project euphonia
MacDonald, R. L., Jiang, P.-P., Cattiau, J., Heywood, R., Cave, R., Seaver, K., Ladewig, M. A., Tobin, J., Brenner, M. P., Nelson, P. C., et al. (2021) · Interspeech 2021 · doi:10.21437/interspeech.2021-697 · Cited by 72 · see citing works
Lea, C., Huang, Z., Narain, J., Tooley, L., Yee, D., Tran, D. T., Georgiou, P., Bigham, J. P., & Findlater, L. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581224 · Cited by 35 · see citing works
KSoF: The Kassel State of Fluency Dataset – A Therapy Centered Dataset of Stuttering
Bayerl, S. P., von Gudenberg, A. W., Hönig, F., Nöth, E., & Riedhammer, K. (2022) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/4vhbgykydx3e · Cited by 31 · see citing works
Analysis and Tuning of a Voice Assistant System for Dysfluent Speech
Mitra, V., Huang, Z., Lea, C., Tooley, L., Wu, S., Botten, D., Palekar, A., Thelapurath, S., Georgiou, P., Kajarekar, S. S., et al. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-2006 · Cited by 28 · see citing works
Gong, R., Xue, H., Wang, L., Xu, X., Li, Q., Xie, L., Bu, H., Wu, S., Zhou, J., Qin, Y., et al. (2024) · Interspeech 2024 · doi:10.21437/interspeech.2024-918 · Cited by 16 · see citing works
Enhancing ASR for Stuttered Speech with Limited Data Using Detect and PassPreprint
Shonibare, O., Tong, X., & Ravichandran, V. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2202.05396 · Cited by 14 · see citing works
Enhancing Stuttering Detection: A Syllable-Level Stutter Dataset
Narasinga, V., Fathima, H., Motepalli, K. S. S., Mahesh, S., Sai Akarsh, C., Barche, P., Mirishkar, G. S., Abraham, A. K., & Vuppala, A. K. (2024) · International Conference on Signal Processing and Communications (SPCOM) · doi:10.1109/spcom60851.2024.10631644 · Cited by 3 · see citing works
AISHELL-Stammertalk 中文口吃数据库 A Mandarin stuttered speech datasetPreprint
Gong, R., Xue, H., Wang, L., et al. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2406.07256
KSoF: The Kassel State of Fluency Dataset -- A Therapy Centered Dataset of Stuttering
Bayerl, S. P., von Gudenberg, A. W., Hönig, F., et al. (2022) · arXiv · doi:10.48550/arxiv.2203.05383
Analysis and Tuning of a Voice Assistant System for Dysfluent Speech
Mitra, V., Huang, Z., Lea, C., Tooley, L., Wu, S., Botten, D., Palekar, A., Thelapurath, S., Georgiou, P., Kajarekar, S., et al. (2021) · arXiv · doi:10.48550/arxiv.2106.11759
Flagged first, then by citations
Research works printed but not linked: 1
- Baevski, A., Zhou, Y., Mohamed, A., & Auli, M. "wav2vec 2.0: A framework for self-supervised learning of speech representations". Advances in neural information processing systems, 33, pp: 12449- 12460, 2020.
Cited by
Sorted by citations
Tang, X., Li, J., & Wu, S. (2026) · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790405 · Cited by 4 · see citing works
“While annotating stuttered speech is inherently challenging and subjective due to its high variability and the ambiguous boundary between stuttered and fluent speech [99, 102], people who stutter (PWS) are rarely involved to address such complexities [6, 58, 83, 102].”
Exploring Whisper Embeddings for Stutter Detection: A Layer-Wise Study
Batra, A., Kar, B., & Das, P. K. (2025) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco63237.2025.11226086 · Cited by 3 · see citing works
Self-citationCripping AI: Reimagining AI Through Lived Disability Experiences
Tang, X., Lin, T.-A., Li, J., & Wu, S. (2026) · Proceedings of the 2026 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3805689.3806744 · Cited by 2 · see citing works
“For much of history, blindness has been defined in terms of a lack of sight, while vision has been positioned as the authoritative source of knowledge [216].”
Multilingual Stutter Event Detection for English, German, and Mandarin Speech
Haas, F., & Bayerl, S. P. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-02548-7_17
Batra, A., Saluja, M. S., Tiwari, P., & Das, P. K. (2025) · IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon66050.2025.11375397
Self-citationLMS-Whisper: Efficient Lightweight Whisper for Multi-Stutter Speech Classification
Ameer, H., Fatima, M., Latif, R., & Latif, S. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11463681
Sultan, M. R., & Chaudry, B. (2026) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi69079.2026.00102
An End to End Speech Stuttering Detection and Correction System Using Efficientnet Framework
Naik, N. M., Kakhandaki, V., Sahana, G., Chikkamath, S., & Budihal, S. V. (2026) · IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT) · doi:10.1109/conecct68978.2026.11700573
Real-Time Stuttering Therapy Using Hybrid Signal-Language Architecture
Kharat, A. R., Padvekar, R., Ghumnani, H., Tripathi, M., & Oswal, S. (2026) · International Conference on Computing Communication Control and automation · doi:10.1109/iccubea69821.2026.11702100
Real-Time Stuttered Speech-to-Text Conversion Using Machine Learning and MLP
Sarika, S., Ismail, A. M., Akshay, S., Arjun, S., & Sayandhana, K. S. (2026) · International Conference on Trends in Engineering Systems and Technologies (ICTEST) · doi:10.1109/ictest69239.2026.11722988
- Authors
- Batra, A., Narang, M., Sharma, N. K., & Das, P. K.
- Affiliations
- Indian Institute of Technology Guwahati (IN)
- Venue
- ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Publisher
- IEEE
- Published
- 2025-04-06
- Pages
- 1-4
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W4408353789
- Cited by
- 8
- References
- 14
- Citations by year
- 2026: 5 · 2025: 3
- FWCI
- 94.71
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE International Conference on Acoustics Speech and Signal Processing (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Topics
- Social Sciences › Psychology › Clinical Psychology › Stuttering Research and Treatment
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Health Sciences › Medicine › Physiology › Voice and Speech Disorders
- Keywords
- stuttering, stuttered speech, stuttering severity, blocks, prolongation, interjections, sound repetition, word repetition, Indian languages, multilingual speech dataset, spontaneous speech, read speech, picture description, speech annotation, speech technology
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