Statistical Machine Translation for Speech: A Perspective on Structures, Learning, and Decoding

In this paper, we survey and analyze state-of-the-art statistical machine translation (SMT) techniques for speech translation (ST). We review key learning problems, and investigate essential model structures in SMT, taking a unified perspective to reveal both connections and contrasts between automa...

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Vydáno v:	Proceedings of the IEEE Ročník 101; číslo 5; s. 1180 - 1202
Hlavní autor:	Zhou, Bowen
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.05.2013 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Automata Consolidation Context awareness Decoding Discriminative training finite-state transducer (FST) Formalism graph hypergraph Information processing Learning Machine translation Speech Speech processing speech translation (ST) Statistical learning statistical machine translation (SMT) Synchronous synchronous context-free grammar (SCFG) Syntax Training Transducers Translations Viterbi algorithm Viterbi search
ISSN:	0018-9219, 1558-2256
On-line přístup:	Získat plný text
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