Open AccessProceedings Article
Document Driven Machine Translation Enhanced Automatic Speech Recognition
Matthias Paulik,Christian Fügen,Thomas Schaaf,Tanja Schultz,Sebastian Stüker,Alex Waibel +5 more
- 01 Jan 2005
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About: This article is published in Conference of the International Speech Communication Association. The article was published on 01 Jan 2005. and is currently open access. The article focuses on the topics: Machine translation.
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Citations
Bilingual LSA-based adaptation for statistical machine translation
TL;DR: A novel approach to cross-lingual language model and translation lexicon adaptation for statistical machine translation (SMT) based on bilingual latent semantic analysis with rapid bootstrapping of LSA models for new languages based on a source LSA model of another language.
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•Proceedings Article
Bilingual-LSA Based LM Adaptation for Spoken Language Translation
Yik-Cheung Tam,Ian R. Lane,Tanja Schultz +2 more
- 01 Jun 2007
TL;DR: A bLSA model is introduced which enables latent topic distributions to be efficiently transferred across languages by enforcing a one-to-one topic correspondence during training and consistently improved machine translation quality on both speech and text based adaptation.
•Proceedings Article
Local lexical adaptation in Machine Translation through triangulation: SMT helping SMT
Josep Maria Crego,Aurélien Max,François Yvon +2 more
- 23 Aug 2010
TL;DR: In this work, predictions are obtained by means of pivoting via auxiliary languages, and introduced into the main SMT system in the form of a low order language model, which is estimated on a sentence-by-sentence basis.
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Survey on Speech, Machine Translation and Gestures in Ambient Assisted Living.
Dimitra Anastasiou
- 01 Jan 2011
TL;DR: The goal is to provide a single uniform multimodal interface combining FOSS speech processing, MT, and gesture recognition tools for people in need.
6
Micro-adaptation lexicale en traduction automatique statistique 1
Josep Maria Crego,Gregor Leusch,Aurélien Max,Hermann Ney,François Yvon +4 more
- 01 Jan 2010
TL;DR: A generic framework in Statistical Machine Translation in which lexical hypotheses, in the form of a target language model local to the input sentence, are used to guide the search for the best translation, thus performing a lexical micro adaptations.