Statistical speech translation system based on voice recognition optimization using multimodal sources of knowledge and characteristics vectors

Synergic combination of different sources of knowledge is a key issue for the development of modern statistical translators. In this work, a speech translation statistical system that adds additional other-than-voice information in a voice translation system is presented. The additional information serves as a base for the log-linear combination of several statistical models. We describe the theoretical framework of the problem, summarize the overall architecture of the system, and show how the system is enhanced with the additional information. Our real prototype implements a real-time speech translation system from Spanish to English that is adapted to specific teaching-related environments.

► The translation can be improved by adding new heuristic characteristics. ► Our speech translation statistical system adds additional information. ► Additional information is mixed with several statistical models. ► Our prototype implements a Spanish-to-English speech translation system. ► The translation system is adapted to specific teaching-related environments.