The EuTRANS project aims at using Example-Based approaches for the automatic development of Machine Translation systems --accepting text and speech input-- for limited domain applications. During the first phase of the project, a speech translation system that is based on the use of automatically learnt Subsequential Transducers has been built. This paper contains a detailed and to a long extent self-contained overview of the transducer learning algorithms and system architecture, along with a new approach for using categories representing words or short phrases in both input and output languages. Experimental results using this approach are reported for a task involving the recognition and translation of sentences in the hotel reception communication domain, with a vocabulary of 683 words in Spanish. A translation word error rate of 1.97% is achieved in real time factor 2.7 in a Personal Computer.

Word clustering methods and, in general, categorization techniques have been successfully used to reduce the number of parameters to be estimated in language and translation models.