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

arXiv: https://arxiv.org/abs/2509.15473 · HTML: https://mlsanity.com/paper/2509.15473/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 17 references that are in the map.
- First posted on arXiv 18 Sep 2025.

## Builds on (most cited first, 9 of 9)

- [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)
- [Layer-wise Analysis of a Self-supervised Speech Representation Model](https://mlsanity.com/paper/2107.04734/) (2021)
- [SEP-28k: A Dataset for Stuttering Event Detection From Podcasts With People Who Stutter](https://mlsanity.com/paper/2102.12394/) (2021)
- [Investigating salient representations and label Variance in Dimensional Speech Emotion Analysis](https://mlsanity.com/paper/2312.16180/) (2023)
- [Pre-Trained Foundation Model representations to uncover Breathing patterns in Speech](https://mlsanity.com/paper/2407.13035/) (2024)
- [Model-driven Heart Rate Estimation and Heart Murmur Detection based on Phonocardiogram](https://mlsanity.com/paper/2407.18424/) (2024)
- [Turbocharge Speech Understanding with Pilot Inference](https://mlsanity.com/paper/2311.17065/) (2023)
- [Foundation Model Hidden Representations for Heart Rate Estimation from Auscultation](https://mlsanity.com/paper/2505.20745/) (2025)

## Abstract (arXiv)

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.