通过突出性分析解开病态声音的复杂性
Abdullah Abdul Sattar Shaikh1, M S Bhargavi1, Ganesh R Naik2
1Department of Computer Science and Engineering, Bangalore Institute of Technology, Bangalore, 560004, Karnataka, India.
Computers in biology and medicine
|October 19, 2023
概括
这项研究介绍了用于分类语音病理的AI模型,如高动力性失声症和逆流性喉炎. 开发的神经网络实现了高精度,为语音障碍诊断提供了一个非侵入性的替代方案.
科学领域:
- 人工智能在医学中的应用
- 语言病理学 语音病理学
- 生物医学信号处理
背景情况:
- 语音障碍显著影响沟通,需要准确的诊断方法.
- 目前用于语音病理的诊断技术可能是侵入性的和昂贵的.
- 现有的语音病理学AI研究主要集中在二进制分类上,忽视了多类场景.
研究的目的:
- 开发和评估人工智能模型,以多类分类语音病理.
- 调查高动力失声症,低动力失声症,逆流性喉炎和健康声音的特征.
- 为了增强从杂的语音数据中提取功能,使用UNet++自动编码器denoisers.
主要方法:
- 提出了三种神经网络架构:多层感知器 (MLP),短时间里埃转换 (STFT) 和Mel频 Cepstral 系数 (MFCC).
- 使用语音ICar fEDerico II (VOICED) 数据集进行培训和验证.
- 采用UNet++自动编码器来降低噪音和提取特征.
主要成果:
- 在143个特征中,MLP模型实现了97.1%的准确性.
- 在STFT模型中显示了99.8%的灵敏度与可比准确度.
- MFCC模型提供了97.1%的准确性,更小的模型大小,以及对逆流性喉炎的改进分类.
结论:
- 人工智能模型,特别是STFT和MFCC,显示出准确的多类语音病理检测的巨大潜力.
- Saliency 分析发现高音声区域对于异常检测至关重要.
- 解决数据限制是推动人工智能驱动的语音障碍分类用于临床应用的关键.
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