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.