Automatic Chewing Ability Assessment Using a Self-Supervised Learning Model

Abstract

Quantitative assessment of chewing ability is essential in healthcare. Current methods, such as using glucose-containing gummies, face challenges in measurement. To mitigate these challenges, a previous study introduced a technique to estimate the amount of glucose extracted based on gummy chewing sounds. In this study, we modify this existing method using a large-scale self-supervised learning model called wavLM to enhance accuracy. Validation on simulated data demonstrated remarkable accuracy improvements using the encoder output of wavLM combined with a one-dimensional convolutional neural network.