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. 1. INTRODUCTION We are pursuing an approach to speech recognition that develops detectors for various distinctive features from their acoustic correlates in the speech signal. Crucial to the success of such an approach are the following: (1) determination of the acoustic signatures for different...

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