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|a 9783540456377
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|a 10.1007/3-540-45637-6
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|a 025.04
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|a Information Retrieval Techniques for Speech Applications
|h [electronic resource] /
|c edited by Anni R. Coden, Eric W. Brown, Savitha Srinivasan.
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|a 1st ed. 2002.
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|a Berlin, Heidelberg :
|b Springer Berlin Heidelberg :
|b Imprint: Springer,
|c 2002.
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|a XII, 116 p.
|b online resource.
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|a Lecture Notes in Computer Science,
|x 0302-9743 ;
|v 2273
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|a Traditional Information Retrieval Techniques -- Perspectives on Information Retrieval and Speech -- Spoken Document Pre-processing -- Capitalization Recovery for Text -- Adapting IR Techniques to Spoken Documents -- Clustering of Imperfect Transcripts Using a Novel Similarity Measure -- Extracting Keyphrases from Spoken Audio Documents -- Segmenting Conversations by Topic, Initiative, and Style -- Extracting Caller Information from Voicemail -- Techniques for Multi-media Collections -- Speech and Hand Transcribed Retrieval -- New Applications -- The Use of Speech Retrieval Systems: A Study Design -- Speech-Driven Text Retrieval: Using Target IR Collections for Statistical Language Model Adaptation in Speech Recognition -- WASABI: Framework for Real-Time Speech Analysis Applications (Demo).
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|a This volume is based on a workshop held on September 13, 2001 in New Orleans, LA, USA as part of the24thAnnualInternationalACMSIGIRConferenceon ResearchandDevelopmentinInformationRetrieval.Thetitleoftheworkshop was: "Information Retrieval Techniques for Speech Applications." Interestinspeechapplicationsdatesbackanumberofdecades.However, it is only in the last few years that automatic speech recognition has left the con?nes of the basic research lab and become a viable commercial application. Speech recognition technology has now matured to the point where speech can be used to interact with automated phone systems, control computer programs, andevencreatememosanddocuments.Movingbeyondcomputercontroland dictation, speech recognition has the potential to dramatically change the way we create,capture,andstoreknowledge.Advancesinspeechrecognitiontechnology combined with ever decreasing storage costs and processors that double in power every eighteen months have set the stage for a whole new era of applications that treat speech in the same way that we currently treat text. The goal of this workshop was to explore the technical issues involved in a- lying information retrieval and text analysis technologies in the new application domainsenabledbyautomaticspeechrecognition.Thesepossibilitiesbringwith themanumberofissues,questions,andproblems.Speech-baseduserinterfaces create di?erent expectations for the end user, which in turn places di?erent - mands on the back-end systems that must interact with the user and interpret theuser'scommands.Speechrecognitionwillneverbeperfect,soanalyses- plied to the resulting transcripts must be robust in the face of recognition errors. The ability to capture speech and apply speech recognition on smaller, more - werful, pervasive devices suggests that text analysis and mining technologies can be applied in new domains never before considered.
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|a Information storage and retrieval.
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|a Natural language processing (Computer science).
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|a Information Storage and Retrieval.
|0 http://scigraph.springernature.com/things/product-market-codes/I18032
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|a Natural Language Processing (NLP).
|0 http://scigraph.springernature.com/things/product-market-codes/I21040
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|a Coden, Anni R.
|e editor.
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|4 http://id.loc.gov/vocabulary/relators/edt
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|a Brown, Eric W.
|e editor.
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|4 http://id.loc.gov/vocabulary/relators/edt
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|a Srinivasan, Savitha.
|e editor.
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|4 http://id.loc.gov/vocabulary/relators/edt
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|a SpringerLink (Online service)
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|t Springer eBooks
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|i Printed edition:
|z 9783662161975
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|i Printed edition:
|z 9783540431565
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|a Lecture Notes in Computer Science,
|x 0302-9743 ;
|v 2273
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|u https://doi.org/10.1007/3-540-45637-6
|z Full Text via HEAL-Link
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|a Computer Science (Springer-11645)
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