Distinctive Feature Detection using Support Vector Machines

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 (SYM) that arise when the structural risk minimization principle is applied to each feature detection problems. In particular, we describe the problem of detecting stop consonants in continuous speech and discus 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 their performances.