Different mother wavelets and pathological voice

Abstract

Early diagnosis of different maladies and pathologies of human vocal system using noninvasive methods and diverse signal processing technics is a problem that is particularly considered by biomedical engineering and signal processing researchers, recently. Automatic detection of voice pathology from speech signal is a new topic and has not been progressed enough. An algorithm able to classify two pathological voice signals based on Wavelet Packets (WP) and Fisher's Linear Discriminant (FLD) is presented in this research. We use WP and different mother wavelets (Daubechies, Coiflet, and Symmlet) for time-frequency analysis giving quantitative evaluation of signal characteristics to identify pathologies in voice signals of subjects with different ages for both male and female. Choosing Coiflet mother wavelet, we use FLD to find the best tree among Coiflet Wavelet Packet trees. After selecting best features from terminal nodes of the best tree with contribution to Genetic Algorithm, we apply Support Vector Machines to separate voice pathologies. Applying our algorithm to seperate polyp from some other pathologies we come to much higher conclusions in contrast to previous works that use Daubechies mother wavelet instead of Coiflet mother wavelet (e.g. 92.5% in comparison to 82.5% for separating polyp from adductor spasmodic dysphonia).