这篇论文用50人的实验数据，结合说话人分离和机器学习，证明了语音能有效检测压力，还找出了关键声学特征，挺有意思的研究。
本研究从50名参与者的语音数据中，利用说话人分离和机器学习模型，实现了对Trier Social Stress Test情境下压力状态的自动检测，性能显著高于均值基线。同时，部分生理和情感压力反应可从声学韵律特征中预测，特征重要性分析识别出最关键的预测因子。结论表明语音可作为压力反应的多维度无侵入式指标。
Automatic Detection of Stress from Speech in the Trier Social Stress Test
Automatically detecting stress in speech provides an unobtrusive way to gain insights relevant to behavioral research or clinical assessment. This study investigates the automatic differentiation between a stressful and non-stressful situation, and the prediction of physiological and affective stress responses. Speech data was collected from 50 participants who either completed the Trier Social Stress Test (TSST) or a non-stressful control condition. With a processing pipeline that included speaker diarization and machine learning models, we achieved stress detection performance significantly above a mean baseline. Moreover, relevant physiological and affective stress responses were partially predictable from acoustic-prosodic features. Feature-importance analyses identified the most informative predictors contributing to model performance. The findings demonstrate that speech can serve as a meaningful and unobtrusive indicator of multiple dimensions of the human stress response.