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Simultaneous Speech and Eating Behavior Recognition Using Data Augmentation and Two-Stage Fine-Tuning.

Toshihiro Tsukagoshi¹, Masafumi Nishida¹, Masafumi Nishimura^1,2

¹Graduate School of Science and Technology, Shizuoka University, 3-5-1 Johoku, Chuo-ku, Hamamatsu 432-8011, Japan.

Sensors (Basel, Switzerland)

|March 17, 2025

Summary

This summary is machine-generated.

This study introduces a novel method for simultaneously recognizing speech and eating behaviors using synthetic data and two-stage fine-tuning. The approach effectively monitors daily health behaviors, achieving high accuracy in detecting chewing and swallowing.

Keywords:

eating behavior recognition health monitoring self-supervised learning skin-contact microphones speech recognition

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Area of Science:

Biomedical Engineering
Acoustic Signal Processing
Machine Learning for Health

Background:

Simultaneous recognition of speech and eating behaviors is crucial for health management.
Existing systems face challenges due to the distinct acoustic and contextual properties of speech and eating sounds.
Integrated recognition for these behaviors is an underexplored area.

Purpose of the Study:

To develop a high-precision method for simultaneously recognizing speech and eating behaviors.
To address the domain adaptation challenges in integrating these two behavioral sound domains.
To enable daily health monitoring through advanced voice recognition technology.

Main Methods:

Data augmentation using synthetic data creation by concatenating speech and eating sounds.
A two-stage fine-tuning approach for adapting self-supervised learning models.
Generation of training data simulating real-world co-occurrence of speech and eating.

Main Results:

Maintained high speech recognition accuracy.
Achieved high detection performance for eating behaviors: F1 score of 0.918 for chewing and 0.926 for swallowing.
Demonstrated the effectiveness of the proposed domain adaptation technique.

Conclusions:

The proposed method successfully integrates speech and eating behavior recognition.
This technology holds significant potential for non-invasive, daily health monitoring applications.
Further research can build upon this approach for comprehensive behavioral analysis.