CUED Publications database

Infinite structured support vector machines for speech recognition

Yang, J and Van Dalen, RC and Zhang, SX and Gales, MJF (2014) Infinite structured support vector machines for speech recognition. In: UNSPECIFIED pp. 3320-3324..

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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. © 2014 IEEE.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Divisions: Div F > Machine Intelligence
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:05
Last Modified: 19 Jul 2018 07:55