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_aInformation Retrieval Techniques for Speech Applications _h[electronic resource] / _cedited by Anni R. Coden, Eric W. Brown, Savitha Srinivasan. |
| 250 | _a1st ed. 2002. | ||
| 264 | 1 |
_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg : _bImprint: Springer, _c2002. |
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_aXII, 116 p. _bonline resource. |
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_aLecture Notes in Computer Science, _x1611-3349 ; _v2273 |
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| 505 | 0 | _aTraditional 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). | |
| 520 | _aThis 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, pervasivedevices suggests that text analysis and mining technologies can be applied in new domains never before considered. | ||
| 650 | 0 | _aInformation storage and retrieval systems. | |
| 650 | 0 | _aNatural language processing (Computer science). | |
| 650 | 1 | 4 | _aInformation Storage and Retrieval. |
| 650 | 2 | 4 | _aNatural Language Processing (NLP). |
| 700 | 1 |
_aCoden, Anni R. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
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| 700 | 1 |
_aBrown, Eric W. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
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| 700 | 1 |
_aSrinivasan, Savitha. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
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| 710 | 2 | _aSpringerLink (Online service) | |
| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9783540431565 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783662161975 |
| 830 | 0 |
_aLecture Notes in Computer Science, _x1611-3349 ; _v2273 |
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| 856 | 4 | 0 | _uhttps://doi.org/10.1007/3-540-45637-6 |
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