Spoken-Language Machine Translation in Limited Domains: Can it be Achieved by Finite-State Models? *

Subsequential transducers constitute a formal model for translation that may be considered perhaps too simple to model translation between natural languages. However, their capability can suffice in limited-domain translation tasks. The finitestate nature of subsequential transducers makes their integration with well-known Continuous Speech Recognition technology both easy and efficient. A recent algorithm allows the automatic learning of these transducers, given a sufficiently large set of examples of sentences and their corresponding translations, and it also allows the incorporation of syntactic restrictions of the input and/or output languages. In this paper, we describe an implementation of a Speech Translation System for limited domains which is fully trainable and capable of real time translation from speech input.