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

arXiv: https://arxiv.org/abs/2510.08586 · HTML: https://mlsanity.com/paper/2510.08586/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 32 references that are in the map.
- First posted on arXiv 2 Oct 2025.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [State-of-the-art Speech Recognition With Sequence-to-Sequence Models](https://mlsanity.com/paper/1712.01769/) (2017)
- [Multi-modal Attention for Speech Emotion Recognition](https://mlsanity.com/paper/2009.04107/) (2020)
- [Beyond Silent Letters: Amplifying LLMs in Emotion Recognition with Vocal Nuances](https://mlsanity.com/paper/2407.21315/) (2024)
- [End-to-End Speech Emotion Recognition: Challenges of Real-Life Emergency Call Centers Data Recordings](https://mlsanity.com/paper/2110.14957/) (2021)
- [MUSER: MUltimodal Stress Detection using Emotion Recognition as an Auxiliary Task](https://mlsanity.com/paper/2105.08146/) (2021)

## Built on by (most cited first, 1 of 1)

- [Explainable graph attention network for stress recognition (StressGAT) via differential action units](https://mlsanity.com/paper/2607.20819/) (2026)

## Abstract (arXiv)

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.