Sameer Maskey, Bowen Zhou, et al.
ICSLP 2006
Our statistical concept-based spoken language translation method consists of three cascaded components: natural language understanding, natural concept generation and natural word generation. In the previous approaches, statistical models are used only in the first two components. In this paper, a novel maximum-entropy-based statistical natural word generation algorithm is proposed that takes into account both the word level and concept level context information in the source and the target language. A recursive generation scheme is further devised to integrate this statistical generation algorithm with the previously proposed maximum-entropy-based natural concept generation algorithm. The translation error rate is reduced by 14%-20% in our speech-to-speech translation experiments.
Sameer Maskey, Bowen Zhou, et al.
ICSLP 2006
Seetharami Seelam, Yanbin Liu, et al.
CCPE
Bowen Zhou, Bing Xiang, et al.
SSST 2008
Sameer R. Maskey, Martin Cmejrek, et al.
SLT 2008