A novel decision function and the associated decision-feedback learning for speech translation

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...

• Type: Journal
• Year: 2011
• Where: ICASSP
• Authors: Yaodong Zhang, Li Deng, Xiaodong He, Alex Acero
• Keywords: Design Methodology, ICASSP 2011, Overall Speech Translation, Signal Processing, Speech Translation