  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.
