面向基于语音的认知障碍数字生物标志物:语音作为认知评估的代理语音
Jonathan Heitz1,2, Ines M Engler1,3, Nicolas Langer4,5
1Department of Psychology, Methods of Plasticity Research, University of Zurich, Zurich, Switzerland.
NPJ digital medicine
|January 31, 2026
概括
自动语音分析显著改善了老年人的认知衰退查. 这种数字生物标志物提供了一个可扩展的,低成本的方法,用于早期检测和监测认知障碍.
科学领域:
- 老年学是一门学科.
- 计算语言学 计算语言学
- 神经学 神经学
背景情况:
- 认知能力下降在老年人群中越来越令人担忧,需要可访问的查工具.
- 早期干预对于管理与年龄相关的认知障碍至关重要.
研究的目的:
- 为了调查自动语音分析作为一个可扩展的代理认知评估在老年人的认知评估.
- 评估语言和声学语音特征在预测认知领域分数中的有效性.
- 开发和验证基于语音的分类器,用于识别认知障碍的个体.
主要方法:
- 机器学习回归模型被用来从语音特征预测认知领域得分.
- 一个二进制分类器被训练来识别低于规范认知值的个体.
- 该方法在一个独立的临床数据集上得到了验证,包括阿尔茨海默病患者.
主要成果:
- 与人口统计数据相比,语音分析模型的预测性能增加了四倍.
- 对于识别认知障碍,二进制分类器实现了高达0.81的ROC-AUC.
- 该方法在不同的数据集和临床人群中证明了可通用性.
结论:
- 自动语音分析是一种临床可行的,低成本的,非侵入性的数字生物标志物.
- 语音分析显示了大规模认知查和临床试验参与者选择的潜力.
- 这项技术可以帮助认知监测和早期发现认知衰退.
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