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Titlebook: Information Retrieval Techniques for Speech Applications; Anni R. Coden,Eric W. Brown,Savitha Srinivasan Conference proceedings 2002 Sprin

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发表于 2025-3-21 19:49:44 | 显示全部楼层 |阅读模式
书目名称Information Retrieval Techniques for Speech Applications
编辑Anni R. Coden,Eric W. Brown,Savitha Srinivasan
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Information Retrieval Techniques for Speech Applications;  Anni R. Coden,Eric W. Brown,Savitha Srinivasan Conference proceedings 2002 Sprin
描述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
出版日期Conference proceedings 2002
关键词Multimedia Information Retrieval; Speech Retrieval; clustering; document processing; information retriev
版次1
doihttps://doi.org/10.1007/3-540-45637-6
isbn_softcover978-3-540-43156-5
isbn_ebook978-3-540-45637-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2002
The information of publication is updating

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0302-9743 eon 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
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Capitalization Recovery for Texton output and closed caption text. The value of these text sources can be greatly enhanced with proper capitalization. We describe and evaluate a series of techniques that can recover proper capitalization. Our final system is able to recover more than 88% of the capitalized words with better than 90% accuracy.
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Speech and Hand Transcribed Retrievalinary documents are generally retrieved in preference to recognised ones. Means of correcting or eliminating the observed bias is the subject of this paper. Initial ideas and some preliminary results are presented.
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Speech-Driven Text Retrieval: Using Target IR Collections for Statistical Language Model Adaptation d to a target collection, we adapt statistical language models used for speech recognition based on the target collection, so as to improve both the recognition and retrieval accuracy. Experiments using existing test collections combined with dictated queries showed the effectiveness of our method.
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Segmenting Conversations by Topic, Initiative, and Stylesegmentations and should be easy to obtain from the audio. Speech style variation at the beginning, middle and end of topics may also be exploited for topical segmentation and would not require the detection of rare keywords.
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Extracting Caller Information from Voicemailthis work, we present two information extraction methods, one based on hand-crafted rules, one based on statistically trained maximum entropy model.We evaluate their performance on both manually transcribed messages and on the output of a speech recognition system.
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