HMM based pyramid match kernel for classification of sequential patterns of speech using support vector machines

Classification of varying length sequences using support vector machine (SVM) requires a suitable kernel that measures the similarity between a pair of sequences. In this paper we propose a novel approach to design a pyramid match kernel (PMK) using hidden Markov model. We study the performance of the SVM-based classifiers using the proposed PMK for recognition of isolated utterances of E-set in English alphabet and recognition of consonant-vowel segments of speech in Hindi and compare with that of the SVM-based classifiers using score-space kernels and alignments kernels.