Dynamic Stress Detection: A Study of Temporal Progression Modelling of Stress in Speech
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910053381963776 |
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| author | Lall, Vishakha Liu, Yisi |
| author_facet | Lall, Vishakha Liu, Yisi |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_08586 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Dynamic Stress Detection: A Study of Temporal Progression Modelling of Stress in Speech Lall, Vishakha Liu, Yisi Audio and Speech Processing Artificial Intelligence Computation and Language Sound 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. |
| title | Dynamic Stress Detection: A Study of Temporal Progression Modelling of Stress in Speech |
| topic | Audio and Speech Processing Artificial Intelligence Computation and Language Sound |
| url | https://arxiv.org/abs/2510.08586 |