用堆叠的元音对声病理进行端到端的深度学习分类
George S Liu1,2, Jordan M Hodges3, Jingzhi Yu4
1Department of Otolaryngology Head and Neck Surgery Stanford University School of Medicine, Stanford University Stanford California USA.
Laryngoscope investigative otolaryngology
|October 30, 2023
概括
与人工智能 (AI) 同时分析多个元音记录可以改善语音障碍的分类. 堆叠的元音模型显示了增强人工智能驱动的声音病理查的前景.
科学领域:
- 计算语言学计算语言学
- 医疗保健中的人工智能
- 语音病理学 语音病理学
背景情况:
- 人工智能 (AI) 技术为使用语音录音作为选工具对语音障碍进行分类提供了潜在的潜力.
- 以前的模型通常依赖于单元母音录音,限制了声病理的预测准确性.
研究的目的:
- 开发和评估人工智能模型,同时分析多个持续的母音录音,以提高声音病理的预测.
- 为了比较堆叠元音模型与基线和堆叠音调模型的性能,用于分类语音障碍.
主要方法:
- 分析了687名健康参与者和334名听障患者 (高功能听障,喉炎) 的语音样本.
- 训练了三种1维卷积神经网络模型:一个基线 (单元),一个堆叠的元音模型 (三个元音同时),和一个堆叠的音调模型 (一个元音跨三个音调).
主要成果:
- 与基线 (0.77) 和堆叠音调 (0.78) 模型相比,堆叠元音模型在多类分类中获得了更高的F1得分 (0.81).
- 与其他模型相比,堆叠的元音模型在分类高功能失声语音样本 (F1分数0.56) 中表现出优异的表现.
结论:
- 同时分析多个持续的母音录音显著改善了人工智能驱动的声音病理的查和分类.
- 堆叠的母音模型架构为增强基于AI的语音障碍检测的准确性提供了一个有希望的方法.
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