自发性言语使得可扩展的数字表型能够对衰老中的身体功能缺陷进行分析
Eloïse Da Cunha1,2,3,4, Raphaël Zory5,6, Frédéric Chorin7
1Université Côte d'Azur, Speech and Language Pathology department of Nice, Faculty of Medicine, Nice, France. eloise.da-cunha@univ-cotedazur.fr.
npj aging
|March 1, 2026
概括
语音分析可以检测到老年人的身体衰退. 机器学习模型使用声学和语言语音特征准确地识别了10个物理领域的缺陷.
科学领域:
- 老年学和数字健康
- 生物标志物和机器学习
背景情况:
- 病态衰老是一个日益增长的全球性挑战,需要早期发现身体衰退,以保持生活质量.
- 目前的物理评估方法可能缺乏广泛查的可访问性和可扩展性.
- 语音分析为评估与衰老相关的生理变化提供了一个潜在的非侵入性途径.
研究的目的:
- 调查多模式语音分析是否可以识别老年人特定的身体功能缺陷.
- 开发和验证机器学习模型,使用语音数据对身体衰退进行分类.
- 探索语音作为数字生物标志物在衰老中的多维物理功能的潜力.
主要方法:
- 对271名社区老年人的自发情感语音录音的声学,语言和时间语音特征的分析.
- 机器学习组合模型的应用,将缺陷分类为十个物理领域 (例如力量,平衡,步态,移动性).
- 利用可解释的人工智能 (SHAP) 来解释语音符号和识别病理生理联系.
主要成果:
- 在所有十个领域的物理功能缺陷中,分类准确度高 (平均AUC=0.91).
- 多模态情感任务堆叠改善了80%的物理测量的检测.
- 确定了与特定缺陷相关的独特的语音改变集群 (词法-语法,神经运动-时间,发音-光谱).
结论:
- 自发语音分析作为一个全面的数字生物标志物用于多维物理功能在衰老.
- 这种方法可以使用智能手机记录进行精确,可访问和可扩展的身体衰老查.
- 开创语音分析作为精密老年病学和服务不足的人群的"身体衰老钟".
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