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arXiv · 2005.11427

A deep-learning based generalized reduced-order model of glottal flow during normal phonation

Yang Zhang·Weili Jiang·Luning Sun·Jianxun Wang·Simeon Smith·Ingo R. Titze·Xudong Zheng·Qian Xue
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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.

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Yang Zhang, Weili Jiang, Luning Sun, Jianxun Wang, Simeon Smith, Ingo R. Titze, Xudong Zheng, Qian Xue. 2020-05-22. A deep-learning based generalized reduced-order model of glottal flow during normal phonation. https://arxiv.org/abs/2005.11427

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