Infinite structured support vector machines for speech recognition

Discriminative models, like support vector machines (SVMs), have been successfully applied to speech recognition and improved performance. A Bayesian non-parametric version of the SVM, the infinite SVM, improves on the SVM by allowing more flexible decision boundaries. However, like SVMs, infinite S...

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Bibliographic Details
Main Authors: Yang, J., van Dalen, R. C., Zhang, S.-X, Gales, M. J. F.
Format: Conference Proceeding
Language:English
Subjects:
Bayesian non-parametrics
Dirichlet process
Equations
Hidden Markov models
infinite structured SVM
Kernel
Mathematical model
mixture of experts
Speech recognition
Support vector machines
Training
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Summary:Discriminative models, like support vector machines (SVMs), have been successfully applied to speech recognition and improved performance. A Bayesian non-parametric version of the SVM, the infinite SVM, improves on the SVM by allowing more flexible decision boundaries. However, like SVMs, infinite SVMs model each class separately, which restricts them to classifying one word at a time. A generalisation of the SVM is the structured SVM, whose classes can be sequences of words that share parameters. This paper studies a combination of Bayesian non-parametrics and structured models. One specific instance called infinite structured SVM is discussed in detail, which brings the advantages of the infinite SVM to continuous speech recognition.
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2014.6854215