把语音中的压力看作动态变化过程，提升检测准确率。
Dynamic Stress Detection: A Study of Temporal Progression Modelling of Stress in Speech
- 从情绪标签推导细粒度压力标注，建模压力随时间演变
- 在MuSE上提升5%，在StressID上提升18%的准确率
- 适合关注心理状态动态监测的研究者与应用开发者
从语音中检测心理压力在高压场景中至关重要。以往研究多将压力视为静态标签，而本工作将压力建模为受历史情绪状态影响的时序演化现象。我们提出一种动态标注策略，从情绪标签推导出细粒度压力标注，并引入基于交叉注意力的序列模型——单向LSTM和Transformer编码器，以捕捉压力的时间演变。该方法在MuSE数据集上相比基线提升5%准确率，在StressID上提升18%，并在自建真实场景数据集上表现出良好泛化能力。结果表明，将压力视为动态构建物能显著提升检测性能。
原文摘要 · Abstract (English)
Detecting psychological stress from speech is critical in high-pressure settings. While prior work has leveraged acoustic features for stress detection, most treat stress as a static label. In this work, we model stress as a temporally evolving phenomenon influenced by historical emotional state. We propose a dynamic labelling strategy that derives fine-grained stress annotations from emotional labels and introduce cross-attention-based sequential models, a Unidirectional LSTM and a Transformer Encoder, to capture temporal stress progression. Our approach achieves notable accuracy gains on MuSE (+5%) and StressID (+18%) over existing baselines, and generalises well to a custom real-world dataset. These results highlight the value of modelling stress as a dynamic construct in speech.
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