Classification of pathological voice into normal/benign/malignant state

Abstract

In this paper we propose a new method to classify the pathological voice into normal, benign and malignant cases. New parameter is proposed to discriminate each class. New parameter is based on cepstral analysis technique. Pathological speech signal is collected at the hospital. Normal speech signal is also contained at the same database and analyzed as well. Then the results are compared to find the differences between normal and pathological speech. Source components are separated using cepstrum after obtaining residual signal from speech. Then the ratios between harmonic components and noise components are obtained from the original signal and residual signal. Finally a neural network is used to train and classify normal, benign and malignant states of speech.