通过声道动态特征识别抑郁新生物标志物
Depression Markers in Speech: An Approach based on Tract Variables Dynamics
- 用李雅普诺夫指数等量化发音的可预测性与复杂度
- 在读诵和自然语料中区分抑郁与正常人群效果显著
- 适合语音分析与精神健康筛查方向研究者参考
本研究基于声道变量的动力学特性，识别出新的抑郁生物标志物，这些变量描述了发音器官的几何配置。该方法首次量化了以往未被探索的发音过程特征——可预测性、复杂性和随机性，分别通过最大李雅普诺夫指数、关联维数和样本熵进行刻画。在公开数据集Androids Corpus上进行了全面实验，包含64名经临床诊断为抑郁的受试者和54名无心理健康史的对照组。结果表明，所提出的生物标志物在读诵和自发语料中均能有效区分抑郁与非抑郁人群，各条件下的Cliff's delta值均较高。
原文摘要 · Abstract (English)
This study identifies new depression biomarkers based on the dynamical properties of tract variables, which represent geometric features describing the configuration of the speech articulators. A key advantage of this approach lies in its ability to quantify aspects of the articulatory process that have not been previously explored in the context of depression, namely predictability, complexity, and randomness. These properties are respectively characterised using the Largest Lyapunov Exponent, the Correlation Dimension, and the Sample Entropy. Thorough experiments were conducted on the Androids Corpus, a publicly available dataset comprising 64 speakers diagnosed with depression by clinicians and 54 control speakers with no reported history of mental health conditions. The results indicate that the proposed biomarkers effectively discriminate between the depressed and control speakers, as evidenced by the high Cliffs delta values across both read and spontaneous speech.
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