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CLC number: TP391.42
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2011-01-31
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An iterative approach to Bayes risk decoding and system combination
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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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pages="204-212",
year="2011",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.C1000045"
}
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%A Hai-hua Xu
%A Jie Zhu
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%DOI 10.1631/jzus.C1000045
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T1 - An iterative approach to Bayes risk decoding and system combination
A1 - Hai-hua Xu
A1 - Jie Zhu
J0 - Journal of Zhejiang University Science C
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EP - 212
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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.
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