Automatic Assessment of Pathological Voice Quality Using Higher-Order Statistics in the LPC Residual Domain

Abstract

A preprocessing scheme based on linear prediction coefficient (LPC) residual is applied to higher-order statistics (HOSs) for automatic assessment of an overall pathological voice quality. The normalized skewness and kurtosis are estimated from the LPC residual and show statistically meaningful distributions to characterize the pathological voice quality. 83 voice samples of the sustained vowel /a/ phonation are used in this study and are independently assessed by a speech and language therapist ( SALT) according to the grade of the severity of dysphonia of GRBAS scale. These are used to train and test classification and regression tree ( CART). The best result is obtained using an optima l decision tree implemented by a combination of the normalized skewness and kurtosis, with an accuracy of 92.9%. It is concluded that the method can be used as an assessment tool, providing a valuable aid to the SALT during clinical evaluation of an overall pathological voice quality. Copyright (C) 2009 J. Lee and M. Hahn. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.