构建高质量失语症语音数据集，提升语音识别模型的可靠性。
Learnings from curating a trustworthy, well-annotated, and useful dataset of disordered English speech
- 扩充语音库多样性，人工校正35万条录音转录文本
- 为超75%说话者添加40+项语音特征元数据
- 揭示自动标注局限性，助力听障语音识别研究
Project Euphonia 是谷歌发起的改善失语症语音自动识别（ASR）的项目。核心目标是构建大规模、高质量且多样化的语音语料库。本文介绍了最新的数据采集与标注进展：扩大数据库中的说话人多样性；对120万音频记录中的35万条添加人工审核的转录修正和音频质量标签；为超过75%的说话者收集包含40多个语音特征标签的综合元数据。报告了转录修正对机器学习研究的影响、失语症模式评估的评分者间差异，并阐述了收集语音元数据的考量。同时讨论了使用现成自动化标注方法评估失语症语音的局限性。
原文摘要 · Abstract (English)
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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