语音数据收集的现行做法和语音人工智能研究的局限性:一项全国调查
Emily Evangelista1, Rohan Kale2, Desiree McCutcheon3
1University of South Florida Morsani College of Medicine, Tampa, Florida, U.S.A.
The Laryngoscope
|December 13, 2023
概括
标准化声学数据管理对于推进语音AI研究至关重要. 目前的做法缺乏统一性,阻碍了多机构的合作研究和开发强大的语音人工智能 (AI) 算法.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能的人工智能
- 语音科学 语言科学
背景情况:
- 语音AI算法的准确性取决于高质量的语音数据.
- 由于缺乏标准化的声数据管理协议,现有的语音数据未得到充分利用.
- 这限制了大规模语音人工智能 (AI) 研究的潜力.
研究的目的:
- 评估北美语音中心语音数据收集,存储和分析当前的做法.
- 识别阻碍协作语音研究的感知局限性.
- 为开发语音数据管理的标准化协议提供信息.
主要方法:
- 一个30个问题的在线调查被分发给北美语音中心的从业者.
- 该调查收集了有关声学数据管理实践和协作障碍的数据.
- 受访者包括喉科专家和语音语言病理学家 (SLPs).
主要成果:
- 只有28%的受访者使用标准化协议来收集和存储声数据.
- 尽管有87%的人进行语音研究,但只有38%的人跨机构合作.
- 关键的局限性包括缺乏标准化的方法 (30%) 和数据准备资源不足 (55%).
结论:
- 需要标准化的声学数据管理,以便使用人工智能进行大规模的多机构语音研究.
- 开发基础设施以确保安全和有效的数据共享至关重要.
- 标准化将提高语音数据集的可用性和有效性,用于人工智能开发.
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