Title :
Detection and classification of voice pathology using feature selection
Author :
Al Mojaly, Malak ; Muhammad, Ghulam ; Alsulaiman, Mansour
Author_Institution :
Dept. of Comput. Eng., King Saud Univ., Riyadh, Saudi Arabia
Abstract :
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.
Keywords :
Gaussian processes; cepstral analysis; feature extraction; feature selection; genetic algorithms; mixture models; set theory; signal classification; speech recognition; statistical testing; support vector machines; ASR mechanism; GMM; Gaussian mixture model; Kruskal-Wallis test; LPCC; MEEI subset database; MFCC; RASTA-PLP; SVM; automatic speech recognition mechanism; feature selection; genetic algorithm; information extraction; linear prediction cepstral coefficients; mel frequency cepstral coefficient; multiclass pathology classification; relative spectra perceptual linear predictive; standard deviation; support vector machine; t-test; voice pathology classification; voice pathology detection; Accuracy; Diseases; Feature extraction; Mel frequency cepstral coefficient; Pathology; Support vector machines; feature selection; support vector machine; voice pathology classification; voice pathology detection;
Conference_Titel :
Computer Systems and Applications (AICCSA), 2014 IEEE/ACS 11th International Conference on
DOI :
10.1109/AICCSA.2014.7073250