Bayesian Inference of Vocal Fold Material Properties from Glottal Area Waveforms Using a 2D Finite Element Model.

Paul J Hadwin,Sean D Peterson,Mohsen Motie-Shirazi,Byron D Erath

doi:10.3390/app9132735

Paul J Hadwin, Sean D Peterson + Show 2 more

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https://doi.org/10.3390/app9132735

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Abstract

Bayesian estimation has been previously demonstrated as a viable method for developing subject-specific vocal fold models from observations of the glottal area waveform. These prior efforts, however, have been restricted to lumped-element fitting models and synthetic observation data. The indirect relationship between the lumped-element parameters and physical tissue properties renders extracting the latter from the former difficult. Herein we propose a finite element fitting model, which treats the vocal folds as a viscoelastic deformable body comprised of three layers. Using the glottal area waveforms generated by self-oscillating silicone vocal folds we directly estimate the elastic moduli, density, and other material properties of the silicone folds using a Bayesian importance sampling approach. Estimated material properties agree with the “ground truth” experimental values to within 3% for most parameters. By considering cases with varying subglottal pressure and medial compression we demonstrate that the finite element model coupled with Bayesian estimation is sufficiently sensitive to distinguish between experimental configurations. Additional information not available experimentally, namely, contact pressures, are extracted from the developed finite element models. The contact pressures are found to increase with medial compression and subglottal pressure, in agreement with expectation.

Highlights

Numerical models have long been employed to better understand the complex physics involved in human phonation
An important observation is that all of the experimental values fall within two standard deviations of the estimated values. This indicates that the use of a finite element (FE) model of the vocal folds (VFs) is statistically capable of inferring accurate estimates of the material properties from a glottal area waveform (GAW)
The approaches employed for developing subject-specific numerical VF models have focused on lumped-elements for the fitting model in the inverse analysis; as such, parameter estimates are often greatly abstracted from the physical tissue properties

Summary

Introduction

Numerical models have long been employed to better understand the complex physics involved in human phonation. Reduced-order and finite element numerical models of the vocal folds (VFs) can self-oscillate in a manner representative of actual VF kinematics during sustained vowels [1], pitch glides [2], and, in a few cases, running speech [3]. Such models have explored a wide range of phenomena relevant to normal and pathological phonation, including the impact of a posterior glottal gap [4], the ventricular folds [5], phonation onset pressure [6], and the efficacy of various compensation mechanisms for vocal hyperfunction [7,8].

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