Martin Sundermeyer
2014
Translation Modeling with Bidirectional Recurrent Neural Networks
Martin Sundermeyer
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Tamer Alkhouli
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Joern Wuebker
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Hermann Ney
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)
2011
Speech recognition for machine translation in Quaero
Lori Lamel
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Sandrine Courcinous
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Julien Despres
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Jean-Luc Gauvain
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Yvan Josse
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Kevin Kilgour
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Florian Kraft
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Viet-Bac Le
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Hermann Ney
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Markus Nußbaum-Thom
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Ilya Oparin
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Tim Schlippe
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Ralf Schlüter
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Tanja Schultz
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Thiago Fraga da Silva
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Sebastian Stüker
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Martin Sundermeyer
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Bianca Vieru
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Ngoc Thang Vu
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Alexander Waibel
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Cécile Woehrling
Proceedings of the 8th International Workshop on Spoken Language Translation: Evaluation Campaign
This paper describes the speech-to-text systems used to provide automatic transcriptions used in the Quaero 2010 evaluation of Machine Translation from speech. Quaero (www.quaero.org) is a large research and industrial innovation program focusing on technologies for automatic analysis and classification of multimedia and multilingual documents. The ASR transcript is the result of a Rover combination of systems from three teams ( KIT, RWTH, LIMSI+VR) for the French and German languages. The casesensitive word error rates (WER) of the combined systems were respectively 20.8% and 18.1% on the 2010 evaluation data, relative WER reductions of 14.6% and 17.4% respectively over the best component system.
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Co-authors
- Hermann Ney 2
- Lori Lamel 1
- Sandrine Courcinous 1
- Julien Despres 1
- Jean-Luc Gauvain 1
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