Abstract
An important aspect of distinctive feature based approaches to
automatic speech recognition is the formulation of a framework for
robust detection of these features. We discuss the application of the
support vector machines (SVM) that arise when the structural risk
minimization principle is applied to such feature detection problems. In
particular, we describe the problem of detecting stop consonants in
continuous speech and discuss an SVM framework for detecting these
sounds. In this paper we use both linear and nonlinear SVMs for stop
detection and present experimental results to show that they perform
better than a cepstral features based hidden Markov model (HMM) system,
on the same task
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