Machine-learning applied to classify flow-induced sound parameters from simulated human voice

TL;DR

This study uses simulated human voice data and machine learning techniques to classify flow-induced sound parameters, aiming to better understand voice disorders and improve diagnostic features.

Contribution

It introduces a hybrid aeroacoustic simulation model and applies machine learning to classify voice disorder characteristics from simulated acoustic parameters.

Findings

Subglottal pressure classified with 91.7% accuracy.

CPP and GC type are key discriminators.

LDA visualization reveals important acoustic features.

Abstract

Disorders of voice production have severe effects on the quality of life of the affected individuals. A simulation approach is used to investigate the cause-effect chain in voice production showing typical characteristics of voice such as sub-glottal pressure and of functional voice disorders as glottal closure insufficiency and left-right asymmetry. Therewith, 24 different voice configurations are simulated in a parameter study using a previously published hybrid aeroacoustic simulation model. Based on these 24 simulation configurations, selected acoustic parameters (HNR, CPP, ...) at simulation evaluation points are correlated with these simulation configuration details to derive characteristic insight in the flow-induced sound generation of human phonation based on simulation results. Recently, several institutions studied experimental data, of flow and acoustic properties and correlated it with healthy and disordered voice signals. Upon this, the study is a next step towards a detailed dataset definition, the dataset is small, but the definition of relevant characteristics are precise based on the existing simulation methodology of simVoice. The small datasets are studied by correlation analysis, and a Support Vector Machine classifier with RBF kernel is used to classify the representations. With the use of Linear Discriminant Analysis the dimensions of the individual studies are visualized. This allows to draw correlations and determine the most important features evaluated from the acoustic signals in front of the mouth. The GC type can be best discriminated based on CPP and boxplot visualizations. Furthermore and using the LDA-dimensionality-reduced feature space, one can best classify subglottal pressure with 91.7\% accuracy, independent of healthy or disordered voice simulation parameters.