Comparative Study on Automatic Speech Recognition

Abstract

Speech is a tools used as a means of communication between society. Along with the developing technology, various methods have been proposed to enable people to communicate and interact with the machines. In this study, Mel Frequency Cepstral Coefficients and Pitch Feature were obtained from the data set consisting of ten classes with different speakers. The obtained features were compared with classification achievements using k-Nearest Neighbor (KNN), Decision Tree (DT) and Quadratic Discriminant Analysis (QDA) classifiers. Furthermore, sensitivity of classifiers used with different numbers of training data is presented.