深度学习使用多元母音Mel谱图对多类语音障碍进行分类
Yimiao Wang1, Shuaichi Ma1,2, Chen Zhao1,2
1ENT Institute and Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan University, Shanghai, People's Republic of China.
Ear, nose, & throat journal
|March 13, 2026
概括
一个新的深度学习模型准确地从非侵入性录音中分类语音障碍. 这种人工智能工具对查高风险喉病变充满希望,改进了传统的诊断方法.
科学领域:
- 人工智能在医学中的应用
- 生物声学是一种生物声学.
- 医学诊断 医学诊断 医学诊断
背景情况:
- 声音障碍很普遍,源于声病变或移动性问题.
- 目前的诊断方法,如喉腔镜,是轻微侵入性的.
- 需要针对语音障碍的非侵入性诊断工具.
研究的目的:
- 开发和验证深度学习 (DL) 模型,用于自动分类语音障碍.
- 该模型使用非侵入性语音录音进行诊断.
- 评估模型在多类和二进制分类任务上的表现.
主要方法:
- 对897名患者和健康对照进行了回顾性分析.
- 持续的普通话元音被记录并转换为Mel谱图.
- 一个基于ResNet和特征金字塔网络的DL模型被训练和评估.
主要成果:
- 三类任务 (正常,病变,移动障碍) 的精度为0.795.5.
- 二元任务 (高风险与良性病变) 的准确度为0.826.
- 该模型显示高灵敏度 (0.933) 检测高风险的喉病变.
结论:
- 分析多元母音Mel光谱的DL模型可以非侵入性地分类语音障碍.
- 该模型实现了临床上有意义的准确性.
- 它显示出作为高风险喉病变查工具的巨大潜力.
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