在帕金森病中具有强大且独立于语言的声学特征
Sabrina Scimeca1, Federica Amato1, Gabriella Olmo1
1Department of Control and Computer Engineering, Polytechnic University of Turin, Turin, Italy.
Frontiers in neurology
|June 29, 2023
概括
语音分析有助于对帕金森病 (PD) 的诊断. 结合持续的发音和句子重复与Mel频率 Cepstral 系数,提供了一种强大的方法来监测跨不同患者群体的PD进展.
科学领域:
- 神经学 神经学
- 语音科学 语言科学
- 生物医学工程 生物医学工程
背景情况:
- 语音样本分析有助于帕金森病 (PD) 的诊断和监测.
- 语音分析是复杂的,受说话者特征和录音条件的影响.
- 语音任务选择影响PD检测中的特征提取和算法性能.
研究的目的:
- 为了评估PD分析不同声乐任务的有效性.
- 为了识别语言,性别和记录方法的变化,强大的语音特征.
- 为了比较特征选择和分类模型,以获得最佳的PD检测管道.
主要方法:
- 利用了六个数据集,其中包括176名健康对照 (HC) 和178名来自不同国籍的PD患者.
- 在各种条件下使用专业麦克风和智能手机记录语音数据.
- 对持续的发音和句子重复任务进行了体内和体内统计分析.
主要成果:
- 组合的持续发音和句子重复任务优于PD检测单个练习.
- 梅尔频率脑系数 (MFCC) 在区分HC和PD方面非常有效.
- 多种框架通信系统在不同的语言和获取技术中表现出强大.
结论:
- 使用组合语音任务的语音协议可以有效地捕捉PD相关的语音变化,患者的努力最小.
- 识别的语音特征对性别,语言和记录方式的依赖最小.
- 允许开发可靠的跨公司工具,用于PD监测,分期和跟进.
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