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Minimum Bayes Risk decoding and system combination based on a recursion for edit distance
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DOI：10.1016/j.csl.2011.03.001.png)
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摘要
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In this paper we describe a method that can be used for Minimum Bayes Risk (MBR) decoding for speech recognition. Our algorithm can take as input either a single lattice, or multiple lattices for system combination. It has similar functionality to the widely used Consensus method, but has a clearer theoretical basis and appears to give better results both for MBR decoding and system combination. Many different approximations have been described to solve the M BR decoding problem, which is very difficult from an optimization point of view. Our proposed method solves the problem through a novel forward backward recursion on the lattice, not requiring time markings. We prove that our algorithm iteratively improves a bound on the Bayes risk. (C) 2011 Elsevier Ltd. All rights reserved.
Keyword:
Speech recognition
Minimum Bayes Risk decoding
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