On the use of support vector machines for phonetic classification
- 1 January 1999
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 2 (15206149) , 585-588 vol.2
- https://doi.org/10.1109/icassp.1999.759734
Abstract
Support vector machines (SVMs) represent a new approach to pattern classification which has 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 nonlinear 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.Keywords
This publication has 8 references indexed in Scilit:
- Applications of Support Vector Machines to Speech RecognitionIEEE Transactions on Signal Processing, 2004
- Phone classification with segmental features and a binary-pair partitioned neural network classifierPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- A Tutorial on Support Vector Machines for Pattern RecognitionData Mining and Knowledge Discovery, 1998
- Heterogeneous acoustic measurements for phonetic classification 1Published by International Speech Communication Association ,1997
- The Nature of Statistical Learning TheoryPublished by Springer Nature ,1995
- Speaker-independent phone recognition using hidden Markov modelsIEEE Transactions on Acoustics, Speech, and Signal Processing, 1989
- Maximum Likelihood from Incomplete Data Via the EM AlgorithmJournal of the Royal Statistical Society Series B: Statistical Methodology, 1977
- Control Methods Used in a Study of the VowelsThe Journal of the Acoustical Society of America, 1952