Abstract
Support vector machines (SVMs) represent a new approach to pattern
classification which has attracted a great deal of interest in the
machine learning community. Their appeal lies in their strong connection
to the underlying statistical learning theory, in particular the theory
of structural risk minimization. SVMs have been shown to be particularly
successful in fields such as image identification and face recognition;
in many problems SVM classifiers have been shown to perform much better
than other nonlinear classifiers such as artificial neural networks and
k-nearest neighbors. This paper explores the issues involved in applying
SVMs to phonetic classification as a first step to speech recognition.
We present results on several standard vowel and phonetic classification
tasks and show better performance than Gaussian mixture classifiers. We
also present an analysis of the difficulties we foresee in applying SVMs
to continuous speech recognition problems
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