Finite-state speech-to-speech translation

A fully integrated approach to Speech-Input Language Translation in limited-domain applications is presented. The mapping from the input to the output language is modeled in terms of a nite state translation model which is learned from examples of input-output sentences of the task considered. This model is tightly integrated with standard acoustic-phonetic models of the input language and the resulting global model directly supplies, through Viterbi search, an optimal output-language sentence for each input-language utterance. Several extensions to this framework, recently developed to cope with the increasing di culty of translation tasks, are reviewed. Finally, results for a task in the framework of hotel front-desk communication, with a vocabulary of about 700 words, are reported.