Pharyngeal health plays a vital role in essential human functions such as breathing, swallowing, and vocalization. Early detection of swallowing abnormalities, also known as dysphagia, is crucial for timely intervention. However, current diagnostic methods often rely on radiographic imaging or invasive procedures. In this study, we propose an automated framework for detecting dysphagia using portable and noninvasive acoustic sensing coupled with applied machine learning. By capturing subtle acoustic signals from the neck during swallowing tasks, we aim to identify patterns associated with abnormal physiological conditions. Our approach achieves promising test-time abnormality detection performance, with an AUC-ROC of 0.904 under 5 independent train-test splits. This work demonstrates the feasibility of using noninvasive acoustic sensing as a practical and scalable tool for pharyngeal health monitoring.
@article{arxiv.2602.02725,
title = {Automated Dysphagia Screening Using Noninvasive Neck Acoustic Sensing},
author = {Jade Chng and Rong Xing and Yunfei Luo and Kristen Linnemeyer-Risser and Tauhidur Rahman and Andrew Yousef and Philip A Weissbrod},
journal= {arXiv preprint arXiv:2602.02725},
year = {2026}
}
Comments
Accepted to 2026 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2026)