Learnings from curating a trustworthy, well-annotated, and useful dataset of disordered English speech

Pan-Pan Jiang; Jimmy Tobin; Katrin Tomanek; Robert L. MacDonald; Katie Seaver; Richard Cave; Marilyn Ladewig; Rus Heywood; Jordan R. Green

arXiv · 2409.09190

Learnings from curating a trustworthy, well-annotated, and useful dataset of disordered English speech

Pan-Pan Jiang·Jimmy Tobin·Katrin Tomanek·Robert L. MacDonald·Katie Seaver·Richard Cave·Marilyn Ladewig·Rus Heywood·Jordan R. Green

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Abstract

Project Euphonia, a Google initiative, is dedicated to improving automatic speech recognition (ASR) of disordered speech. A central objective of the project is to create a large, high-quality, and diverse speech corpus. This report describes the project's latest advancements in data collection and annotation methodologies, such as expanding speaker diversity in the database, adding human-reviewed transcript corrections and audio quality tags to 350K (of the 1.2M total) audio recordings, and amassing a comprehensive set of metadata (including more than 40 speech characteristic labels) for over 75\% of the speakers in the database. We report on the impact of transcript corrections on our machine-learning (ML) research, inter-rater variability of assessments of disordered speech patterns, and our rationale for gathering speech metadata. We also consider the limitations of using automated off-the-shelf annotation methods for assessing disordered speech.

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Pan-Pan Jiang, Jimmy Tobin, Katrin Tomanek, Robert L. MacDonald, Katie Seaver, Richard Cave, Marilyn Ladewig, Rus Heywood, Jordan R. Green. 2024-09-13. Learnings from curating a trustworthy, well-annotated, and useful dataset of disordered English speech. https://arxiv.org/abs/2409.09190

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