Abstract for clarkson_icassp99

In Proceedings IEEE International Conference on Speech and Signal Processing, Phoenix, USA, 1999

ON THE USE OF SUPPORT VECTOR MACHINES FOR PHONETIC CLASSIFICATION

P.R. Clarkson and P.J. Moreno

March 1999

Support Vector Machines (SVMs) represent a new approach to pattern classification which has recently 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 non-linear 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.


(ftp:) clarkson_icassp99.ps.gz (http:) clarkson_icassp99.ps.gz
PDF (automatically generated from original PostScript document - may be badly aliased on screen):
  (ftp:) clarkson_icassp99.pdf | (http:) clarkson_icassp99.pdf

If you have difficulty viewing files that end '.gz', which are gzip compressed, then you may be able to find tools to uncompress them at the gzip web site.

If you have difficulty viewing files that are in PostScript, (ending '.ps' or '.ps.gz'), then you may be able to find tools to view them at the gsview web site.

We have attempted to provide automatically generated PDF copies of documents for which only PostScript versions have previously been available. These are clearly marked in the database - due to the nature of the automatic conversion process, they are likely to be badly aliased when viewed at default resolution on screen by acroread.