Detecting and Interpreting Acoustic Features Using Support Vector Machines

Partha Niyogi; Chris Burges. 16 January, 2002.
Communicated by Partha Niyogi.


An important aspect of distinctive feature based approaches to automatic speech recognition is the formulation of a framework for the 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 consider in some detail the problem of detecting stop consonants in continuous speech. We track dynamic acoustic properties and discuss and SVM framework for detecting these sounds. In this paper, we use both linear and non-linear SVMs and present experimental results to illustrate the factors upon which superior performance depends. We also relate the detectors to perceptual phenomena like cateogorical perception and the perceptual magnet effect. We show how the detectors operate by comparing sounds in a transformed space leading to many different distance metrics that may then be defined. Only one of these is compatible with the perceptual magnet effect.

Original Document

The original document is available in Postscript (uploaded 16 January, 2002 by Partha Niyogi).