Non-invasive pathological voice classifications using linear and non-linear classifiers

Abstract

In this research work, a non-invasive method is conducted to diagnose the voice diseases through acoustic analysis of voice signal. Three feature extraction methods are proposed based on the time-domain energy variations, Mel frequency cepstral coefficients combined with singular value decomposition and wavelet packet and entropy features. Linear classifier namely LDA based classifier and non-linear classifiers such as k-NN classifier, MLP network, PNN, and GRNN are suggested to discriminate pathological voices from normal voices. In this research work, three databases such as MEEI voice disorders database, MAPACI Speech Pathology database, and dataset-III (collected at Hospital Tuanku Fauziah, Kangar, Perlis) are used to test the independence of the algorithms to the databases and the proposed feature extraction algorithms are also tested in noisy condition at 30dB signal-to-noise ratio. Two types of experiments are conducted using the proposed feature extraction and classification algorithms. In the first experiment, classification of normal and pathological voice has been investigated. In the second experiment, the detection of the specific type of voice disorders has been carried out through twoclass pattern classification problems. The different kind of voice disorders are selected such as AP squeezing, vocal fold edema and vocal fold paralysis based on the previous research works. The experiment investigations elucidate that the proposed feature extraction algorithms give very promising classification accuracy for the classification of normal and pathological voices under controlled and noisy environment. In the case of detection of specific disorders, wavelet packet and entropy features perform well compared to time-domain energy variations based features and MFCCs and SVD based features. The following performance measures such as positive predictivity, specificity, sensitivity, and overall accuracy have been considered, in order to test the reliability and effectiveness of the linear and non-linear classifiers. For the MEEI voice disorders database, the success rate of the classifiers is above 98% for the classification of normal and pathological voices and for the detection of specific disorders the best classification accuracy of 100% is achieved. The experiments have also been repeated for the MAPACI speech pathology database and dataset- III under controlled and noisy environment. The results indicate that the wavelet packet and entropy based features provides better classification accuracy compared to time-domain energy based features and MFCCs and SVD based features for the two more databases. It is concluded that proposed feature extraction and classification algorithms can be employed to help the medical professionals for early investigation of voice disorders.