Adoption of Support Vector Machine and Independent Component Analysis for Implementation of Speech Recognizer

음성인식기 구현을 위한 SVM과 독립성분분석 기법의 적용

In this paper we propose effective speech recognizer through recognition experiments for three feature parameters(PCA, ICA and MFCC) using SVM(Support Vector Machine) classifier In general, SVM is classification method which classify two class set by finding voluntary nonlinear boundary in vector space and possesses high classification performance under few training data number. In this paper we compare recognition result for each feature parameter and propose ICA feature as the most effective parameter