September 22-25, 1997
This paper extends previous work exploring the use of Subsequential Transducers to perform speech-input translation in limited-domain tasks. This is done following an integrated approach in which a Subsequential Transducer replaces the input-language model of a conventional speech recognition system, and is used both as language and translation model. This way, the search for the recognised sentence also produces the corresponding translation. A corpus-based approach is adopted in order to build the required models from training data. Experimental results are presented for the translation task considered in the EUTRANS project: one in the hotel domain with more than 500 words per language and language perplexities near to 10.
Bibliographic reference. Amengual, Juan Carlos / Benedi, Jose Miguel / Beulen, Klaus / Casacuberta, Francisco / Castano, Asuncion / Castellanos, Antonio / Jimenez, Victor M. / Llorens, David / Marzal, Andres / Ney, Hermann / Prat, Federico / Vida, Enrique / Vila, Juan Miguel (1997): "Speech translation based on automatically trainable finite-state models", In EUROSPEECH-1997, 1439-1442.