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Titlebook: Robustness in Language and Speech Technology; Jean-Claude Junqua,Gertjan Noord Book 2001 Springer Science+Business Media B.V. 2001 Parsing

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发表于 2025-3-21 18:51:52 | 显示全部楼层 |阅读模式
书目名称Robustness in Language and Speech Technology
编辑Jean-Claude Junqua,Gertjan Noord
视频video
丛书名称Text, Speech and Language Technology
图书封面Titlebook: Robustness in Language and Speech Technology;  Jean-Claude Junqua,Gertjan Noord Book 2001 Springer Science+Business Media B.V. 2001 Parsing
描述In this book we address robustness issues at the speechrecognition and natural language parsing levels, with a focus onfeature extraction and noise robust recognition, adaptive systems,language modeling, parsing, and natural language understanding. Thisbook attempts to give a clear overview of the main technologies usedin language and speech processing, along with an extensivebibliography to enable topics of interest to be pursued further. Italso brings together speech and language technologies often consideredseparately. ..Robustness in Language and Speech Technology. serves as avaluable reference and although not intended as a formal universitytextbook, contains some material that can be used for a course at thegraduate or undergraduate level.
出版日期Book 2001
关键词Parsing; cognition; language; natural language; proving; speech processing; speech recognition
版次1
doihttps://doi.org/10.1007/978-94-015-9719-7
isbn_softcover978-90-481-5643-6
isbn_ebook978-94-015-9719-7Series ISSN 1386-291X Series E-ISSN 2542-9388
issn_series 1386-291X
copyrightSpringer Science+Business Media B.V. 2001
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Robust Parsing of Word Graphs,word graph, provided an appropriate multi-dimensional score function is defined..Finally, a number of techniques is discussed which approximate the fully general search algorithm and experimental results are provided indicating that efficient approximations are possible with little or no reduction in concept accuracy.
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Introduction,s concerning such things as timetables for public transportation. For example, you might ask the system the departure time of a train from Brussels to Amsterdam, specifying that you wish to arrive in Amsterdam at 11. Before proceeding to answer your query such a system will probably ask you on which
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Speaker Compensation in Automatic Speech Recogniton,en the training and testing conditions are similar. However, the word accuracy dramatically degrades if the training and testing conditions differ: Well-known factors that affect the word error rate (WER) are background noise and channel and speaker variability..This chapter deals with the latter (i
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Robustness in Statistical Language Modeling: Review and Perspectives,ced on the spoken utterances, or more generally when the lexical, syntactic, or semantic characteristics of the discourse in the training and testing tasks differ. Obstacles to robustness involve the dual issues of model coverage and parameter reliability, which are intricately related to the qualit
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Regular Approximation of Context-Free Grammars through Transformation,s to any context-free grammar and guarantees that the result can be compiled into a finite automaton. The resulting grammar contains at most one new nonterminal for any nonterminal symbol of the input grammar. The result thus remains readable and if necessary modifiable. We extend the approximation
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Robust Parsing and Beyond,actually produced by end-users in various situations..While early work in robust analysis dates back to the seventies, the topic has recently gained broader attention, due to the large quantities of textual data now available in electronic form (e.g. technical documentation, news, web pages, email,
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