Discrete wavelet transform and support vector machine applied to pathological voice signals identification

An algorithm able to classify pathological and normal voice signals based on Daubechies discrete wavelet transform (DWT-db) and support vector machines (SVM) classifier is presented. DWT-db is used for time-frequency analysis giving quantitative evaluation of signal characteristics to identify pathologies in voice signals, particularly nodules in vocal folds, of subjects with different ages for both male and female. After using a linear prediction coefficients (LPC) filter, the signals mean square values of a particular scale from wavelet analysis are entries to a nonlinear least square support vector machine (LS-SVM) classifier, which leads to an adequate larynx pathology classifier which over 95% of classification accuracy.

1. Introduction

A Coustically, pathologies related to the glottal tract are identified through perceptual standards like breathiness, hoarseness and harshness [1] [4]. However, due to the complex structure of the biological system for speech synthesis, pathologies with harsh characteristics may be confused with that perceptually defined as hoarse [5].