Digital biomarkers for depression have largely relied on static acoustic descriptors, pooled summary statistics, or conventional machine learning representations. Such approaches may miss nonlinear temporal organization embedded in conversational vocal dynamics. We hypothesized that depression is associated with altered recurrence structure in vocal state trajectories, reflecting changes in how the vocal system revisits acoustic states over time. Using the depression subset of the DAIC-WOZ corpus with 142 labeled participants, we modeled frame-level COVAREP trajectories as nonlinear dynamical systems and derived recurrence-based biomarkers from 74 vocal channels. Logistic regression with feature selection and stratified cross-validation evaluated classification performance. Recurrence-based biomarkers achieved a mean cross-validated AUC of 0.689, exceeding static acoustic baselines, entropy-dynamics features, Hurst exponent features, determinism features, and Lyapunov-like instability proxies. Permutation testing indicated statistical significance with $p=0.004$. Pooled cross-validated predictions yielded AUC 0.665 with a 95\% bootstrap confidence interval of [0.568, 0.758]. These findings suggest that depression may be characterized by altered recurrence structure in conversational vocal dynamics and support nonlinear state-space analysis as a promising direction for digital psychiatric biomarkers.

翻译：数字生物标志物在抑郁症检测中主要依赖静态声学描述符、汇总统计特征或传统的机器学习表征。这类方法可能忽视嵌入对话语音动态中的非线性时间组织。我们假设抑郁与声学状态轨迹中递归结构的改变相关，反映了发音系统随时间重复访问声学状态方式的变化。利用包含142名标注参与者的DAIC-WOZ语料库抑郁子集，我们将帧级COVAREP轨迹建模为非线性动力系统，并从74个语音通道中提取基于递归的生物标志物。采用特征选择与分层交叉验证的逻辑回归评估分类性能。基于递归的生物标志物实现了平均交叉验证AUC为0.689，优于静态声学基线、熵动力学特征、赫斯特指数特征、确定性特征及类李雅普诺夫不稳定性代理指标。置换检验显示统计显著性（$p=0.004$）。汇总交叉验证预测的AUC为0.665，95%自助法置信区间为[0.568, 0.758]。这些发现表明抑郁症可能表现为对话语音动态中递归结构的改变，并支持非线性状态空间分析作为数字精神病学标志物研究的一个有前景方向。