A deep-learning based generalized reduced-order model of glottal flow during normal phonation
Abstract
This paper proposes a deep-learning based generalized reduced-order model (ROM) that can provide a fast and accurate prediction of the glottal flow during normal phonation. The approach is based on the assumption that the vibration of the vocal folds can be represented by a universal kinematics equation (UKE), which is used to generate a glottal shape library. For each shape in the library, the ground truth values of the flow rate and pressure distribution are obtained from the high-fidelity Navier-Stokes (N-S) solution. A fully-connected deep neural network (DNN)is then trained to build the empirical mapping between the shapes and the flow rate and pressure distributions. The obtained DNN based reduced-order flow solver is coupled with a finite-element method (FEM) based solid dynamics solver for FSI simulation of phonation. The reduced-order model is evaluated by comparing to the Navier-Stokes solutions in both statics glottal shaps and FSI simulations. The results demonstrate a good prediction performance in accuracy and efficiency.
Cite
@article{arxiv.2005.11427,
title = {A deep-learning based generalized reduced-order model of glottal flow during normal phonation},
author = {Yang Zhang and Weili Jiang and Luning Sun and Jianxun Wang and Simeon Smith and Ingo R. Titze and Xudong Zheng and Qian Xue},
journal= {arXiv preprint arXiv:2005.11427},
year = {2020}
}