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Weighted Finite-State Transducer Inference for Limited-Domain Speech-to-Speech Translation

  • Diamantino Caseiro
  • Isabel Trancoso
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3960)

Abstract

A speech input machine translation system based on weighted finite state transducers is presented. This system allows for a tight integration of the speech recognition with the machine translation modules. Transducer inference algorithms to automatically learn the translation module are also presented. Good experimental results confirmed the adequacy of these techniques to limited-domain tasks. In particular, the reordering algorithm proposed showed impressive improvements by reducing the error rate in excess of 50%.

Keywords

Speech Recognition Machine Translation Automatic Speech Recognition Translation Model Speech Recognition System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Diamantino Caseiro
    • 1
  • Isabel Trancoso
    • 1
  1. 1.L2F INESC-ID/ISTPortugal

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