机器学习辅助语音分析用于早期检测帕金森病:关于扬声器腹和分类技术的研究
Michele Giuseppe Di Cesare1, David Perpetuini1, Daniela Cardone1
1Department of Engineering and Geology, University G. D'Annunzio of Chieti-Pescara, 65127 Pescara, Italy.
Sensors (Basel, Switzerland)
|March 13, 2024
概括
机器学习使用语音记录准确地检测到帕金森病 (PD). 使用ML分析MFCC和GTCC等语音特征,实现了92.3%的准确性,通过手机实现了早期PD检测.
科学领域:
- 神经学 神经学
- 语音信号处理 语音信号处理
- 机器学习 机器学习
背景情况:
- 帕金森病 (PD) 呈现出运动和非运动症状,包括声乐障碍.
- 在PD中声声障碍源于喉和声道肌肉的神经控制变化.
- 使用机器学习 (ML) 的语音分析显示了PD检测的前景.
研究的目的:
- 开发和评估一种基于ML的方法,用于使用语音分析早期检测帕金森病.
- 为了研究MEL频率面系数 (MFCC) 和Gammatone频率面系数 (GTCC) 对于PD鉴定的有效性.
主要方法:
- 利用了伦敦国王学院 (MDVR-KCL) 的移动设备语音录音数据集,包括自发的电话对话.
- 应用扬声器日记化来分割对话中的语音数据.
- 使用MFCC和GTCC用于ML分类的提取语音特征.
主要成果:
- 应用于MFCC和GTCC的机器学习模型在分类帕金森病患者的测试准确率达到92.3%.
- 这项研究证明了ML在从语音模式中识别PD方面的有效性.
结论:
- 语音分析利用MFCC和GTCC与ML提供了一个非常准确的方法来早期检测PD.
- 手机可以作为可访问的,非侵入性的工具,用于具有成本效益的PD查,潜在地改善患者的治疗结果.
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