用语音自动检测压力，可无感评估心理状态。
Automatic Detection of Stress from Speech in the Trier Social Stress Test
- 基于语音的声学特征分析压力状态
- 在50人样本上实现显著高于基线的识别率
- 适合心理学研究与临床压力评估场景
自动从语音中检测压力为行为研究或临床评估提供了无侵入式方法。本研究探讨了在应激与非应激情境间的自动区分，以及生理和情绪压力反应的预测。数据来自50名参与者，分别完成Trier社交压力测试（TSST）或非应激对照任务。通过包含说话人分离和机器学习模型的处理流程，实现了显著优于平均基线的压力检测性能。同时，相关生理与情绪压力反应可部分由声学-韵律特征预测。特征重要性分析揭示了对模型表现贡献最大的预测因子。结果表明，语音可作为人类压力反应多维度的有意义且无感指标。
原文摘要 · Abstract (English)
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.
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