Local Discriminant Wavelet Packet Basis for Voice Pathology Classification

Diagnosis of pathological voice is one of the most important issues in biomedical applications of speech technology. There are some approaches for separating pathological from normal voice signals but a few ones are sophisticated to separate two or more kinds of speech pathologies from each other. This paper introduces an algorithm to discriminate voice pathologies signals from each other via adaptive growth of wavelet packet tree, based on the criterion of local discriminant bases (LDB). Moreover, genetic algorithm is employed for selecting the best feature set and Support Vector Machines as classifier to obtain as much as possible better results. To evaluate the proposed approach, we apply our algorithm to separate polyp from some other pathologies like keratosis leukoplakia, adductor spasmodic dysphonia and etc. Experimental results show the superior performance of this combinational approach against its incomplete versions, i.e. in the case of separating polyp and nodule, the proposed approach leads to 85% performance against 80% for where only complete wavelet packet features without applying GA algorithm are used