Vocal folds pathologies classification using Naïve Bayes Networks

Abstract

in this study the Nave Bayes Network NBN classifier is used for automatic vocal folds pathologies detection and classification. The proposed method is based on the acoustic parameters extraction such as Mel Frequency Cepstral Coefficient (MFCC), jitter, shimmer and fundamental frequency which are used as inputs to NBN classifier to discriminate between three different groups: speakers with normal voice, speakers with spasmodic dysphonia and speakers with vocal folds paralysis. For classification we used a variety of voice simples (signal of vowels production) containing simples of the three groups mentioned. Our study is developed around Saarbruecken Voice Database (SVD) it is an open German database containing deferent samples, words, sentences of normal and pathological voice. The classification rate of the developed detection system is 90%.