Some results with a trainable speech translation and understanding system

Abstract

The problems of limited-domain spoken language translation and understanding are considered. A standard continuous speech recognizer is extended for using automatically learnt finite-state transducers as translation models. Understanding is considered as a particular case of translation where the target language is a formal language. From the different approaches compared, the best results are obtained with a fully integrated approach, in which the input language acoustic and lexical models, and (N-gram) language models of input and output languages, are embedded into the learnt transducers. Optimal search through this global network obtains the best translation for a given input acoustic signal.