Incremental adaptation of speech-to-speech translation

Authors:
Nguyen Bach;Roger Hsiao;Matthias Eck;Paisarn Charoenpornsawat;Stephan Vogel;Tanja Schultz;Ian Lane;Alex Waibel;Alan W. Black
Affiliations:
Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA
Venue:
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
Year:
2009

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Abstract

In building practical two-way speech-to-speech translation systems the end user will always wish to use the system in an environment different from the original training data. As with all speech systems, it is important to allow the system to adapt to the actual usage situations. This paper investigates how a speech-to-speech translation system can adapt day-to-day from collected data on day one to improve performance on day two. The platform is the CMU Iraqi-English portable two-way speech-to-speech system as developed under the DARPA TransTac program. We show how machine translation, speech recognition and overall system performance can be improved on day 2 after adapting from day 1 in both a supervised and unsupervised way.