A Sequential Pattern Classifier Based on Hidden Markov Kernel Machine and Its Application to Phoneme Classification

This paper describes a novel classifier for sequential data based on nonlinear classification derived from kernel methods. In the proposed method, kernel methods are used for enhancing the emission probability density functions (pdfs) of hidden Markov models (HMMs). Because the emission pdfs enhanced by kernel methods have sufficient nonlinear classification performance, mixture models such as Gaussian mixture models (GMMs), which might cause problems of overfitting and local optima, are not necessary in the proposed method. Unlike the methods used in earlier studies on sequential pattern classification using kernel methods, our method can be regarded as an extension of conventional HMMs, and therefore, it can completely model the transition of hidden states with the observed vectors. Therefore, our method can be applied to many applications developed with conventional HMMs, especially for speech recognition. In this paper, we carried out an isolated phoneme classification as a preliminary experiment in order to evaluate the efficiency of the proposed sequential pattern classifier. We confirmed that the proposed method achieved steady improvements as compared to conventional HMMs with Gaussian-mixture emission pdfs trained by the maximum likelihood and the maximum mutual information procedures.