用语音动态熵值识别抑郁症，比传统方法更准。
Entropy-Dominated Temporal Vocal Dynamics as Digital Biomarkers for Depression Detection
- 通过语音熵值捕捉对话中的动态变化，替代静态特征
- 熵特征使检测准确率提升至AUC 0.646（显著优于基准）
- 适合做心理评估的时序数字生物标志物研究者参考
自动抑郁检测常依赖对话信号的静态聚合，可能掩盖临床有意义的行为动态。本研究在DAIC-WOZ语料库上检验熵驱动的时间生物标志物是否能超越标准聚合特征。基于142名标注参与者，重建了话语级声学轨迹，比较了聚合基线、轨迹动态、香农熵生物标志物、递归量化、样本熵、分形复杂度及耦合标志物，在防泄露验证下表现。静态聚合的AUC为0.593，轨迹动态提升至0.637，熵标志物实现最强统计显著提升（AUC 0.646；嵌套交叉验证AUC 0.615；置换检验p=0.017）。熵标志物优于递归、耦合、样本熵与分形特征，多个标志物跨折叠稳定。结果表明，抑郁相关信号更可能存在于对话动态的熵而非平均声学水平中，支持时间敏感的数字表型用于心理健康评估。
原文摘要 · Abstract (English)
Automated depression detection often relies on static aggregation of conversational signals, potentially obscuring clinically meaningful behavioral dynamics. We investigated whether entropy-driven temporal biomarkers improve depression detection beyond standard pooled features using the DAIC-WOZ corpus. Using 142 labeled participants, we reconstructed utterance-level acoustic trajectories and compared pooled temporal baselines, trajectory dynamics, Shannon entropy biomarkers, recurrence quantification, sample entropy, fractal complexity, and coupling biomarkers under leakage-aware validation. Static pooling achieved an AUC of 0.593, trajectory dynamics improved performance to 0.637, and entropy biomarkers produced the strongest statistically significant improvement over pooled baselines (AUC 0.646; nested cross-validated AUC 0.615; permutation p = 0.017). Entropy biomarkers outperformed recurrence, coupling, sample entropy, and fractalbased features, with several biomarkers stable across folds. These findings suggest depression-related signal may lie less in average acoustic levels than in entropy of conversational dynamics, supporting temporally informed digital phenotypes for mental-health assessment.
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