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Quaero Speech-to-Text and Text Translation Evaluation Systems

In: High Performance Computing in Science and Engineering '10

Author

Listed:
  • Sebastian Stüker

    (Karlsruhe Institute of Technology, Research Group 3-01 ‘Multilingual Speech Recognition’)

  • Kevin Kilgour

    (Karlsruhe Institute of Technology, Research Group 3-01 ‘Multilingual Speech Recognition’)

  • Jan Niehues

    (Karlsruhe Institute of Technology, Interactive Systems Laboratories)

Abstract

Our laboratory has used the HP XC4000, the high performance computer of the federal state Baden-Wnrttemberg, in order to participate in the second Quaero evaluation for automatic speech recognition (ASR) and Machine Translation (MT). State-of-the-art automatic speech recognition and machine translation systems train use stochastic models which are trained on large amounts of training data using techniques from the field of machine learning. Using these techniques the systems search for the most likely speech recognition hypothesis, translation hypothesis respectively. The 2009 evaluation systems are further developments of the 2008 evaluation systems which incorporate more training data and updated models. The speech recognition and machine translation models were, at leas in part, trained on the XC4000 high performance cluster. The speech recognition evaluation itself was also mainly executed on the XC4000. In this paper we report on the newly developed system and how we utilized the XC4000 in order to train their models and to run the actual evaluation.

Suggested Citation

  • Sebastian Stüker & Kevin Kilgour & Jan Niehues, 2011. "Quaero Speech-to-Text and Text Translation Evaluation Systems," Springer Books, in: Wolfgang E. Nagel & Dietmar B. Kröner & Michael M. Resch (ed.), High Performance Computing in Science and Engineering '10, pages 529-542, Springer.
    • Handle: RePEc:spr:sprchp:978-3-642-15748-6_38
    DOI: 10.1007/978-3-642-15748-6_38
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