Constraining model-based reasoning using contexts
L. Gong, D. Riecken
WI 2003
This paper describes our recent improvements to IBM TRANSTAC speech-to-speech translation systems that address various issues arising from dealing with resource-constrained tasks, which include both limited amounts of linguistic resources and training data, as well as limited computational power on mobile platforms such as smartphones. We show how the proposed algorithms and methodologies can improve the performance of automatic speech recognition, statistical machine translation, and text-to-speech synthesis, while achieving low-latency two-way speech-to-speech translation on mobiles. © 2011 Elsevier Ltd. All rights reserved.
L. Gong, D. Riecken
WI 2003
Julia Rubin, Krzysztof Czarnecki, et al.
SPLC 2013
Katherine Panciera, Reid Priedhorsky, et al.
CHI 2010
Rie Kubota Ando
CoNLL 2006