Minimum Bayes Risk Estimation and Decoding in Large Vocabulary Continuous Speech Recognition

  • Authors:

  • William Byrne

  • Affiliations:

  • The author is with the Cambridge University Engineering Department, Trumpington Street, Cambridge CB2 1PZ, U.K. E-mail: wjb31@cam.ac.uk

  • Venue:
  • IEICE - Transactions on Information and Systems
  • Year:
  • 2006

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Abstract

Minimum Bayes risk estimation and decoding strategies based on lattice segmentation techniques can be used to refine large vocabulary continuous speech recognition systems through the estimation of the parameters of the underlying hidden Markov models and through the identification of smaller recognition tasks which provides the opportunity to incorporate novel modeling and decoding procedures in LVCSR. These techniques are discussed in the context of going 'beyond HMMs', showing in particular that this process of subproblem identification makes it possible to train and apply small-domain binary pattern classifiers, such as Support Vector Machines, to large vocabulary continuous speech recognition.