Pathological voice classification based on the features of an asymmetric fluid–structure interaction vocal cord model

Abstract

Currently, the acoustic features used for classifying pathological voices do not take the physiological structure and vibration characteristics of the vocal cords into account. We proposed a feature extraction method based on the asymmetric fluid–structure interaction vocal cord model. The airflow viscous force was introduced into this model. The left and right asymmetric coefficients of the upper mass and the lower mass were designed to characterize the vibration mechanism of the diseased vocal cord. Then a hybrid simulated annealing genetic algorithm (HSAGA) optimized the vocal cord model inversion parameters to match the target voice source. The vocal cord model parameters corresponding to the actual voice signal were extracted and combined with the phonation pressure threshold and the phonation flow threshold to classify pathological voices. The experimental results (deposited in the Massachusetts Eye and Ear Infirmary (MEEI) database) showed that the weighted average relative error of the objective optimization function classifying the vibration characteristics of the vocal cords with lesions calculated by the HSAGA was 4.0%. When classifying pathological voices, the accuracy of the vocal cord model parameters was 77.18%, which is 33.18% higher than classifications using acoustic features, and effectively distinguishes different types of pathological voice.