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Advantages of System Combination for Spoken Language Translation

Evgeny Matusov

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ISBN 10: 3838120124 / ISBN 13: 9783838120126
Published by Sudwestdeutscher Verlag Fur Hochschulschriften AG
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236 pages. Dimensions: 8.7in. x 5.9in. x 0.5in.Automatic translation of spoken language is a challenging task that involves several natural language processing (NLP) software modules such as automatic speech recognition (ASR) and machine translation (MT) systems. In recent years, statistical approaches to both ASR and MT were proven to be effective on a large number of translation tasks. Yet the systems involved in speech translation are often developed independently of each other. This work explains how a significant improvement of speech translation quality can be obtained by enhancing the interface between various statistical NLP systems involved in the task of translating human speech. The whole pipeline is considered: ASR, automatic sentence segmentation, machine translation using several systems which take single best or multiple ASR hypotheses as input and employ different translation models, combination of different MT systems. The coupling between the various components is reached through combination of model scores andor hypotheses as well as through development of new and modifications of existing algorithms to handle ambiguous input or to meet the constraints of the downstream components. This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN. Bookseller Inventory # 9783838120126

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Bibliographic Details

Title: Advantages of System Combination for Spoken ...

Publisher: Sudwestdeutscher Verlag Fur Hochschulschriften AG

Binding: Paperback

Book Condition:New

Book Type: Paperback

About this title

Synopsis:

Automatic translation of spoken language is a challenging task that involves several natural language processing (NLP) software modules such as automatic speech recognition (ASR) and machine translation (MT) systems. In recent years, statistical approaches to both ASR and MT were proven to be effective on a large number of translation tasks. Yet the systems involved in speech translation are often developed independently of each other. This work explains how a significant improvement of speech translation quality can be obtained by enhancing the interface between various statistical NLP systems involved in the task of translating human speech. The whole pipeline is considered: ASR, automatic sentence segmentation, machine translation using several systems which take single best or multiple ASR hypotheses as input and employ different translation models, combination of different MT systems. The coupling between the various components is reached through combination of model scores and/or hypotheses as well as through development of new and modifications of existing algorithms to handle ambiguous input or to meet the constraints of the downstream components.

About the Author:

Evgeny Matusov defended his Ph.D. in computer science from RWTH Aachen University, Germany in 2009. His research interest is machine translation of text and speech. He authored and co-authored more than 20 reviewed publications in international conferences, two journal publications, and received the ISCA Best Student Paper Award in 2005.

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