Identification of vocal fold pathology based on Mel Frequency Band Energy Coefficients and singular value decomposition

Many approaches have been developed to detect the vocal fold pathology. Among the approaches, analysis of speech has proved to be an excellent tool for vocal fold pathology detection. This paper presents the Mel Frequency Band Energy Coefficients (MFBECs) combined with singular value decomposition (SVD) based feature extraction method for the classification of pathological or normal voice. In order to extract the most relevant information from the original MFBECs feature dataset, SVD is used. For the analysis, the speech samples of pathological and healthy subjects from the Massachusetts Eye and Ear Infirmary (MEEI) database are used. A simple k-means nearest neighbourhood (k-NN) and Linear Discriminant Analysis (LDA) based classifiers are used for testing the effectiveness of the MFBECs-SVD based feature vector. The experimental results show that the proposed features gives very promising classification accuracy and also can be effectively used to detect the pathological voices clinically.