Dynamic Stress Detection: A Study of Temporal Progression Modelling of Stress in Speech

• URL: http://arxiv.org/abs/2510.08586v1
• Date: Thu, 02 Oct 2025 06:30:44 GMT
• Title: Dynamic Stress Detection: A Study of Temporal Progression Modelling of Stress in Speech
• Authors: Vishakha Lall, Yisi Liu,
• Abstract summary: We model stress as a temporally evolving phenomenon influenced by historical emotional state.<n>We propose a dynamic labelling strategy that fine-grained stress annotations from emotional labels.<n>Our approach achieves notable accuracy gains on MuSE and StressID over existing baselines.
• Score: 1.3320917259299652
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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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