Finite-state transducers for speech-input translation

Abstract

Nowadays, hidden Markov models (HMMs) and n-grams are the basic components of the most successful speech recognition systems. In such systems, HMMs (the acoustic models) are integrated into a n-gram or a stochastic finite-state grammar (the language model). Similar models can be used for speech translation, and HMMs (the acoustic models) can be integrated into a finite-state transducer (the translation model). Moreover, the translation process can be performed by searching for an optimal path of states in the integrated network. The output of this search process is a target word sequence associated to the optimal path. In speech translation, HMMs can be trained from a source speech corpus, and the translation model can be learned automatically from a parallel training corpus. This approach has been assessed in the framework of the EUTRANS project, founded by the European Union. Extensive speech-input experiments have been carried out with translations from Spanish to English and from Italian to English translation, in an application involving the interaction (by telephone) of a customer with a receptionist at the front-desk of a hotel. A summary of the most relevant results are presented in this paper.