Re-examining the robustness of voice features in predicting depression: Compared with baseline of confounders

A large proportion of Depression Disorder patients do not receive an effective diagnosis, which makes it necessary to find a more objective assessment to facilitate a more rapid and accurate diagnosis of depression. Speech data is easy to acquire clinically, its association with depression has been studied, although the actual predictive effect of voice features has not been examined. Thus, we do not have a general understanding of the extent to which voice features contribute to the identification of depression. In this study, we investigated the significance of the association between voice features and depression using binary logistic regression, and the actual classification effect of voice features on depression was re-examined through classification modeling. Nearly 1000 Chinese females participated in this study. Several different datasets was included as test set. We found that 4 voice features (PC1, PC6, PC17, PC24, PNagelkerke's R2). In classification modeling, voice data based model has consistently higher predicting accuracy(F-measure) than the baseline model of demographic data when tested on different datasets, even across different emotion context. F-measure of voice features alone reached 81%, consistent with existing data. These results demonstrate that voice features are effective in predicting depression and indicate that more sophisticated models based on voice features can be built to help in clinical diagnosis.

绝大多数抑郁障碍患者未能获得有效诊断，因此亟需开发更为客观的评估手段，以助力抑郁症的快速精准诊断。语音数据在临床场景中易于获取，其与抑郁症的关联已得到相关研究探索，但语音特征的实际预测效能尚未得到系统验证，因此学界对语音特征在抑郁症识别中所能发挥的作用程度尚未形成普遍认知。本研究采用二元逻辑回归分析，探究了语音特征与抑郁症之间关联的显著性，并通过分类建模重新验证了语音特征用于抑郁症分类的实际效能。本研究共有近千名中国女性受试者参与，且纳入多组不同的数据集作为测试集。本研究筛选出4项语音特征：PC1、PC6、PC17、PC24以及Nagelkerke决定系数R²。在分类建模实验中，基于语音数据的模型在不同测试数据集（甚至不同情绪情境下）的预测性能（F测度）始终优于以人口统计学数据构建的基准模型。仅使用语音特征时，模型的F测度可达81%，与已有研究数据相符。上述结果证实了语音特征在抑郁症预测中的有效性，同时表明可基于语音特征构建更为精密的模型，以辅助临床诊断工作。