A Novel GMM-Based Feature Reduction for Vocal Fold Pathology Diagnosis

Acoustic analysis is a proper method in vocal fold pathology diagnosis so that it can complement and in some cases replace the other invasive, based on direct vocal fold observation, methods. There are different approaches and algorithms for vocal fold pathology diagnosis. These algorithms usually have three stages which are Feature Extraction, Feature Reduction and Classification. While the third stage implies a choice of a variety of machine learning methods (Support Vector Machines, Gaussian Mixture Model, etc.), the first and second stages play a critical role in performance and accuracy of the classification system. In this study we present initial study of feature extraction and feature reduction in the task of vocal fold pathology diagnosis. A new type of feature vector, based on wavelet packet decomposition and Mel-Frequency-Cepstral-Coefficients (MFCCs), is proposed. Also a new method for feature reduction is proposed and compared with conventional methods such as Principal Component Analysis (PCA), F-Ratio and Fisher’s discriminant ratio. Gaussian Mixture Model is used as a classifier for evaluating the performance of the proposed method. The results show the priority of the proposed method in comparison with current methods.