8th European Conference on Speech Communication and Technology

Geneva, Switzerland
September 1-4, 2003


Improving Statistical Natural Concept Generation in Interlingua-Based Speech-to-Speech Translation

Liang Gu, Yuqing Gao, Michael Picheny

IBM T.J. Watson Research Center, USA

Natural concept generation is critical to statistical interlingua-based speech translation performance. To improve maximum-entropy-based concept generation, a set of novel features and algorithms are proposed including features enabling model training on parallel corpora, employment of confidence thresholds and multiple sets of features. The concept generation error rate is reduced by 43%-50% in our speech translation corpus within limited domains. Improvements are also achieved in our experiments on speech-to-speech translation.

Full Paper

Bibliographic reference.  Gu, Liang / Gao, Yuqing / Picheny, Michael (2003): "Improving statistical natural concept generation in interlingua-based speech-to-speech translation", In EUROSPEECH-2003, 2769-2772.