Boli: A dataset for understanding stuttering experience and analyzing stuttered speech

TL;DR

Project Boli introduces a comprehensive, multi-lingual stuttered speech dataset from India, including detailed annotations and metadata, to facilitate research and development in understanding and treating stuttering.

Contribution

It provides the first extensive, annotated, multi-lingual stuttered speech dataset focused on Indian languages, with detailed metadata and analysis tools.

Findings

Dataset includes diverse speech samples and annotations.

Analysis reveals patterns in stuttering severity and types.

Open access enables further research and technology development.

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.