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CLC number: TP391.42

On-line Access: 2011-03-09

Received: 2010-03-03

Revision Accepted: 2010-07-06

Crosschecked: 2011-01-31

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Journal of Zhejiang University SCIENCE C 2011 Vol.12 No.3 P.204-212

http://doi.org/10.1631/jzus.C1000045


An iterative approach to Bayes risk decoding and system combination


Author(s):  Hai-hua Xu, Jie Zhu

Affiliation(s):  Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Corresponding email(s):   haihua_xu@sjtu.edu.cn, zhujie@sjtu.edu.cn

Key Words:  Bayes risk (BR), Confusion network, Speech recognition, Lattice rescoring, System combination


Hai-hua Xu, Jie Zhu. An iterative approach to Bayes risk decoding and system combination[J]. Journal of Zhejiang University Science C, 2011, 12(3): 204-212.

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author="Hai-hua Xu, Jie Zhu",
journal="Journal of Zhejiang University Science C",
volume="12",
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T1 - An iterative approach to Bayes risk decoding and system combination
A1 - Hai-hua Xu
A1 - Jie Zhu
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.C1000045


Abstract: 
We describe a novel approach to bayes risk (BR) decoding for speech recognition, in which we attempt to find the hypothesis that minimizes an estimate of the BR with regard to the minimum word error (MWE) metric. To achieve this, we propose improved forward and backward algorithms on the lattices and the whole procedure is optimized recursively. The remarkable characteristics of the proposed approach are that the optimization procedure is expectation-maximization (EM) like and the formation of the updated result is similar to that obtained with the confusion network (CN) decoding method. Experimental results indicated that the proposed method leads to an error reduction for both lattice rescoring and lattice-based system combinations, compared with CN decoding, confusion network combination (CNC), and ROVER methods.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

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