1. Learnings from curating a trustworthy, well-annotated, and useful dataset of disordered English speech
- Author
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Jiang, Pan-Pan, Tobin, Jimmy, Tomanek, Katrin, MacDonald, Robert L., Seaver, Katie, Cave, Richard, Ladewig, Marilyn, Heywood, Rus, and Green, Jordan R.
- Subjects
Electrical Engineering and Systems Science - Audio and Speech Processing ,Computer Science - Sound - 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., Comment: Interspeech 2024
- Published
- 2024