A Deep-Learning Based Generalized Empirical Flow Model of Glottal Flow During Normal Phonation

Abstract

Abstract This paper proposes a deep-learning based generalized empirical flow model (EFM) 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 EFM is coupled with a finite-element method (FEM) based solid dynamics solver for flow-structure-interaction (FSI) simulation of phonation. The EFM is evaluated by comparing to the N-S solutions in both static glottal shapes and FSI simulations. The results demonstrate a good prediction performance in accuracy and efficiency.