DETECTION AND CLASSIFICATION OF VOICE PATHOLOGY USING FEATURE SELECTION

The aim of this study is to apply automatic speech recognition (ASR) mechanism to improve the amount of information extracted from the voice and to increase the accuracy of the system by using selective highly discriminative features among different types of acoustic features. For feature extraction, we applied three techniques which are Mel Frequency Cepstral Coefficient (MFCC), Linear Prediction Cepstral Coefficients (LPCC), and RelAtive SpecTrA - Perceptual Linear Predictive (RASTA-PLP) with a number of selected coefficients from each technique by using t-test, Kruskal-Wallis test, or genetic algorithm (GA). Then for classification, either support vector machine (SVM) or Gaussian Mixture Model (GMM) is used. The experimental results on a selected MEEI subset database show that the proposed method gives high accuracies compared with some recent related methods both in detection and classification tasks. The highest accuracy of 99.9875 % with a standard deviation of 0.0263 is achieved in case of detection, and 99.8578 % with a standard deviation of 0.1657 in case of multi-class pathology classification.