Interspeech'2005 - Eurospeech

Lisbon, Portugal
September 4-8, 2005

Use of Maximum Entropy in Natural Word Generation for Statistical Concept-Based Speech-to-Speech Translation

Liang Gu, Yuqing Gao

IBM T.J. Watson Research Center, Yorktown Heights, NY, USA

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-entropybased 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-tospeech translation experiments.

Full Paper

Bibliographic reference.  Gu, Liang / Gao, Yuqing (2005): "Use of maximum entropy in natural word generation for statistical concept-based speech-to-speech translation", In INTERSPEECH-2005, 3189-3192.