A two-stage approach using Gaussian mixture models and higher-order statistics for a classification of normal and pathological voices

Abstract

A two-stage classifier is used to improve the classification performance between normal and pathological voices. A primary classification between normal and pathological voices is achieved by the Gaussian mixture model (GMM) log-likelihood scores. For samples that do not meet the thresholds for normal or disordered voice in the GMM, the final decision is made by a higher-order statistics (HOS)-based parameter. The normalized skewness and kurtosis, and means of the normalized skewness and kurtosis were estimated using a sustained vowel /a/ from 53 normal and 173 pathological voices taken from the Disordered Voice Database. Mel-frequency cepstral coefficients (MFCC)-based GMM, the HOS methods, and a two-stage classifier based on the GMM-HOS were performed for each voice signal. A Mann-Whitney rank sum test was used to detect differences in the means of the HOS-based parameters. A fivefold cross-validation scheme was performed to test the classification method. When 16 Gaussian mixtures were used, the MFCC-based GMM algorithm is performed with 92.0% accuracy. When means of the normalized skewness and kurtosis were used, performances of 82.31 and 83.67% were obtained, respectively. The two-stage classifier with 16 Gaussian mixtures and the mean of the normalized kurtosis classified samples with a 96.96% accuracy were obtained. The proposed two-stage classifier is more accurate than the MFCC-based GMM and HOS methods alone and shows potential for the classification of voices in the clinic. © 2012 Lee; licensee Springer.