自动语音分析用于检测老年人的认知衰退
Lihe Huang1,2, Hao Yang2,3, Yiran Che1,2
1School of Foreign Studies, Tongji University, Shanghai, China.
Frontiers in public health
|August 23, 2024
概括
在语音分析中,语言特征在查阿尔茨海默病 (AD) 和轻度认知障碍 (MCI) 中比声学特征更有效. 这项研究使用中国语音数据验证了认知障碍的自动诊断模型.
科学领域:
- 神经学 神经学
- 计算语言学 计算语言学
- 老年学是一门学科.
背景情况:
- 语音分析显示了早期阿尔茨海默病 (AD) 和轻度认知障碍 (MCI) 检测的前景.
- 声学和语言特征是常用的,但它们的比较有效性,特别是中国人口老龄化,尚不清楚.
研究的目的:
- 为了比较声学,语言和综合语音特征的查有效性,用于AD和MCI检测.
- 开发和验证用于认知障碍诊断的自动化中文语音分析模型.
主要方法:
- 92名中国参与者 (AD,MCI,健康对照组) 接受了语言任务和认知查 (MoCA-B).
- 提取了声学 (音调,动,MFCC) 和语言 (POS标签,TTR) 的特征.
- 使用机器学习算法 (SVM,RF) 来比较分类准确度.
主要成果:
- 在区分认知状态时,语言特征通常比声学特征更准确.
- 支持矢量机器 (SVM) 在HC与HC相比实现了80.77%的准确性. 使用组合特征的AD.
- 随机森林 (RF) 仅使用语言特征实现了HC与AD/MCI的80.43%准确率.
结论:
- 语言特征对于在中国人中自动认知障碍诊断非常有效.
- 自动语音分析是中国人口认知健康评估的验证工具.
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