Objective Pathological Voice Quality Assessment Based on HOS Features

Abstract

SUMMARY 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%.

An efficient approach using HOS-based parameters in the LPC residual domain to classify breathy and rough voices

“…In this paper, new parameters to describe the characteristics of breathy and rough voices are introduced. In previous studies, HOSs have shown promising results in the characterization of pathological voices [24,25]. Also, in a large number of published papers, the LPC residual has been shown to be able to classify between normal and pathological voices [14,28].…”

“…While previous work in the area of speech analysis, voicing classification, and pitch estimation have attempted to exploit parameters based on the HOSs of speech signals, little has been done in providing an analytical framework for disordered voice analysis [20][21][22][23][24][25]. Alonso et al proposed the following HOS-based parameters: the interference value of the bicoherence index, the relative values of the high and low frequency energy of the onedimensional bicoherence index, the value of their interference, the variation of the estimated noise by means of the module of the bispectrum, the variation of the noise obtained through the same technique taking into account in this case the phase information, and the interference of the kurtosis [23].…”

“…The CART method was shown to be an effective method to discriminate between normal and disordered voices, with 92.7% accuracy. Subsequently we introduced the HOS-based parameters to improve the classification performance among normal, grade 1, grade 2, and grade 3 voices classified in the GRBAS scale [25]. The HOS-based parameters used were the means, variances, and perturbations of the skewness and kurtosis.…”

“…CART can be used to effectively combine multiple parameters [24,25,30,31]. Especially, compared with other methods like HMM, GMM, and LDA, it is powerful because it can deal with incomplete and small size data.…”

Feature Extraction of Elderly Signals based on Bicoherence Estimation for Automated Medical Diagnosis System

“…If it is a little larger deviation from an average value, it may have an effect on the performance degradation of voice analysis and recognition [14]- [15]. In case of most welfare smart devices, the elderly signals have been neglected due to interface which does not take into account the elderly [6].…”