An Optimal Contact-Mechanically Consistent and Flow-Separation Adapted Modeling of Vocal Fold Dynamics

2026-06-27

AI Summary

This study addresses the limitations of existing single-degree-of-freedom vocal fold models, which struggle to sustain oscillations under structural damping and fail to accurately reproduce glottal closure. The authors propose a physically consistent reduced-order model that, for the first time, simultaneously incorporates flow separation-adaptive aerodynamic resistance and a structurally grounded contact force during closure—without requiring coupling to the vocal tract. Model parameters are calibrated using glottal area waveforms extracted from high-speed videoendoscopy data via deep learning-based segmentation, combined with particle swarm optimization. The governing dynamical equations are solved using a fourth-order Runge–Kutta scheme. Validation across four subjects demonstrates normalized errors below 3%, accurately capturing subject-specific vibratory and closure characteristics while maintaining high computational efficiency and precision.

Abstract

Single mass-spring-damper models of vocal folds have been effective in simulating vocal fold vibrations without added complexity. However, single-degree-of-freedom models cannot sustain oscillation in the presence of structural damping unless source-tract interaction is considered. Moreover, existing lumped models struggle to accurately simulate vocal fold closure during phonation. This study aims to develop a reliable and simplified single-degree-of-freedom model of phonation that can simulate sustained oscillation in a damped system without incorporating a vocal tract model. Additionally, the proposed model maintains vocal fold closure in a manner consistent with the physics of phonation, addressing a longstanding challenge in existing lumped models. High-speed videoendoscopy (HSV) data from four normophonic subjects producing sustained vowel /i/ were used to extract glottal area waveforms (GAWs) via deep learning-based image segmentation for particle swarm optimization of the model parameters. An additional resistance force was incorporated to compensate for flow separation and generate the force imbalance required for sustained oscillation. An external structural force was also added during closure to sustain the closed phase. The 4th-order Runge-Kutta method was used to solve the governing equations with enhanced numerical stability and accuracy. The model parameters were optimized for individual subjects, resulting in normalized errors below 3% between experimental and simulated GAWs. The proposed model accurately reproduced subject-specific vocal fold vibrations and vocal fold closure in agreement with experimental data. Overall, the proposed model provides a computationally efficient framework for simulating sustained phonation without requiring complex source-tract coupling while capturing the key biomechanical and aerodynamic mechanisms of phonation.

Problem

• vocal fold dynamics
• sustained oscillation
• structural damping
• glottal closure
• lumped models

Innovation

• single-degree-of-freedom model
• flow separation compensation
• vocal fold closure
• particle swarm optimization
• glottal area waveform