A comparison of string kernels and discrete hidden Markov models on a Spanish digit recognition task

Abstract

String kernels have been introduced recently in an attempt to apply support vector machine (svm) classifiers to variable-length sequential data from a discrete alphabet. They have been used in the areas of text classification and bioinformatics, where notable results have been obtained. In the present paper string kernels are applied to a Spanish digit recognition task and their performance is compared to that of discrete hidden Markov models (dhmm). It is found that string kernels produce comparable results and may offer an alternative discriminative approach for certain speech recognition tasks.