In this paper we report our recent development of an end-to-end integrative

design methodology for speech translation. Specifically,

a novel decision function is proposed based on the Bayesian analysis,

and the associated discriminative learning technique is presented

based on the decision-feedback principle. The decision function in

our end-to-end design methodology integrates acoustic scores, language

model scores and translation scores to refine the translation

hypotheses and to determine the best translation candidate. This

Bayesian-guided decision function is then embedded into the training

process that jointly learns the parameters in speech recognition

and machine translation sub-systems in the overall speech translation

system. The resulting decision-feedback learning takes a functional

form similar to the minimum classification error training. Experimental

results obtained on the IWSLT DIALOG 2010 database

showed that the proposed system outperformed the baseline system

in terms of BLEU score by 2.3 points.

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In  ICASSP

Publisher  IEEE

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