Investigation of improved approaches to bayes risk decoding |
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Authors: | Hai-hua Xu Jie Zhu |
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Institution: | Department of Electronic Engineering, Shanghai Jiaotong University |
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Abstract: | Bayes risk (BR) decoding methods have been widely investigated in the speech recognition area due to its flexibility and complexity
compared with the maximum a posteriori (MAP) method regarding to minimum word error (MWE) optimization. This paper investigates
two improved approaches to the BR decoding, aiming at minimizing word error. The novelty of the proposed methods is shown
in the explicit optimization of the objective function, the value of which is calculated by an improved forward algorithm
on the lattice. However, the result of the first method is obtained by an expectation maximization (EM) like iteration, while
the result of the second one is achieved by traversing the confusion network (CN), both of which lead to an optimized objective
function value with distinct approaches. Experimental results indicate that the proposed methods result in an error reduction
for lattice rescoring, compared with the traditional CN method for lattice rescoring. |
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Keywords: | |
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