Objective Pathological Voice Quality Assessment Based on HOS Features

Abstract

This work proposes new features to improve the pathological voice quality classification performance. They are the means. the variances, and the perturbations of the higher-order statistics (HOS) such as the skewness and the kurtosis. The HOS-based features show meaningful differences among normal. grade 1, grade 2, and grade 3 voices, classified in the GRBAS scale. The jitter. the shimmer, the harmonic-to-noise ratio (HNR). and the variance of the short-time energy are utilized as the conventional features. The performances are measured by the classification and regression tree (CART) method. Specifically, the CART-based method by utilizing both the conventional features and the HOS-based ones shows its effectiveness in the pathological voice quality Measurement. with the classification accuracy of 87.8%.