Learnings from curating a trustworthy, well-annotated, and useful dataset of disordered English speech
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916393903980544 |
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| author | Jiang, Pan-Pan Tobin, Jimmy Tomanek, Katrin MacDonald, Robert L. Seaver, Katie Cave, Richard Ladewig, Marilyn Heywood, Rus Green, Jordan R. |
| author_facet | Jiang, Pan-Pan Tobin, Jimmy Tomanek, Katrin MacDonald, Robert L. Seaver, Katie Cave, Richard Ladewig, Marilyn Heywood, Rus Green, Jordan R. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_09190 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learnings from curating a trustworthy, well-annotated, and useful dataset of disordered English speech Jiang, Pan-Pan Tobin, Jimmy Tomanek, Katrin MacDonald, Robert L. Seaver, Katie Cave, Richard Ladewig, Marilyn Heywood, Rus Green, Jordan R. Audio and Speech Processing Sound 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. |
| title | Learnings from curating a trustworthy, well-annotated, and useful dataset of disordered English speech |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2409.09190 |