Breathing and Semantic Pause Detection and Exertion-Level Classification in Post-Exercise Speech

• URL: http://arxiv.org/abs/2509.15473v1
• Date: Thu, 18 Sep 2025 22:39:34 GMT
• Title: Breathing and Semantic Pause Detection and Exertion-Level Classification in Post-Exercise Speech
• Authors: Yuyu Wang, Wuyue Xia, Huaxiu Yao, Jingping Nie,
• Abstract summary: Post-exercise speech contains rich physiological and linguistic cues, often marked by semantic pauses, breathing pauses, and combined breathing-semantic pauses.<n>We provide systematic annotations of pause types and conduct exploratory breathing and semantic pause detection and exertion-level classification across deep learning models.<n>Results show per-type detection accuracy up to 89$%$ for semantic, 55$%$ for breathing, 86$%$ for combined pauses, and 73$%$overall, while exertion-level classification achieves 90.5$%$ accuracy, outperformin prior work.
• Score: 33.39650261642241
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Post-exercise speech contains rich physiological and linguistic cues, often marked by semantic pauses, breathing pauses, and combined breathing-semantic pauses. Detecting these events enables assessment of recovery rate, lung function, and exertion-related abnormalities. However, existing works on identifying and distinguishing different types of pauses in this context are limited. In this work, building on a recently released dataset with synchronized audio and respiration signals, we provide systematic annotations of pause types. Using these annotations, we systematically conduct exploratory breathing and semantic pause detection and exertion-level classification across deep learning models (GRU, 1D CNN-LSTM, AlexNet, VGG16), acoustic features (MFCC, MFB), and layer-stratified Wav2Vec2 representations. We evaluate three setups-single feature, feature fusion, and a two-stage detection-classification cascade-under both classification and regression formulations. Results show per-type detection accuracy up to 89$\%$ for semantic, 55$\%$ for breathing, 86$\%$ for combined pauses, and 73$\%$overall, while exertion-level classification achieves 90.5$\%$ accuracy, outperformin prior work.

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