Technology Category
Natural Language Processing: Language Grounding & Multi-modal NLPMachine Learning: Multimodal LearningApplication Domains: Humanities & Computational Social Science
Boli: A dataset for understanding stuttering experience and analyzing stuttered speech
🤖 AI Summary
High-quality, multilingual stuttering speech data—particularly for Indian languages—is critically scarce, hindering both clinical research and technological development. Method: We introduce the first publicly available, multilingual, multimodal Indian stuttering speech dataset. It includes anonymized demographic information, a Stuttering Impact Scale questionnaire, dual-task audio recordings (Rainbow Passage reading and spontaneous image description), and expert linguistic pathology annotations of five stuttering event types: blocks, prolongations, interjections, and sound/word repetitions. Crucially, we integrate subjective self-reported impact assessment with objective phonetic annotation to establish a dual-dimensional analytical framework—linking daily-life impact with clinical severity. Results: Following rigorous de-identification and technical validation of audio quality, the dataset is openly released. It provides foundational support for investigating stuttering mechanisms, developing multilingual speech technologies, and standardizing cross-lingual data collection protocols.
Technology Category
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
Stuttering Speech
Data Scarcity
Indian Languages
Innovation
Methods, ideas, or system contributions that make the work stand out.
Multilingual Stuttering Database
Indian Languages Stuttering Research
High-quality Resource for Stuttering Technology Development
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