An initial attempt for phoneme recognition using Structured Support Vector Machine (SVM)

Abstract

Structured Support Vector Machine (SVM) is a recently developed extension of the very successful SVM approach, which can efficiently classify structured pattern with maximized margin. This paper presents an initial attempt for phoneme recognition using structured SVM. We simply learn the basic framework of HMMs in configuring the structured SVM. In the preliminary experiments with TIMIT corpus, the proposed approach was able to offer an absolute performance improvement of 1.33% over HMMs even with a highly simplified initial approach, probably because of the concept of maximized margin of SVM. We see the potential of this approach because of the high generality, high flexibility, and high power of structured SVM.