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One-sided measures for evaluating ranked retrieval effectiveness with spontaneous conversational speech
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Source Annual ACM Conference on Research and Development in Information Retrieval archive
Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval table of contents
Seattle, Washington, USA
POSTER SESSION: Posters table of contents
Pages: 673 - 674  
Year of Publication: 2006
ISBN:1-59593-369-7
Authors
Baolong Liu  University of Maryland, College Park, MD
Douglas W. Oard  University of Maryland, College Park, MD
Sponsors
SIGIR: ACM Special Interest Group on Information Retrieval
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

Early speech retrieval experiments focused on news broadcasts, for which adequate Automatic Speech Recognition (ASR) accuracy could be obtained. Like newspapers, news broadcasts are a manually selected and arranged set of stories. Evaluation designs reflected that, using known story boundaries as a basis for evaluation. Substantial advances in ASR accuracy now make it possible to build search systems for some types of spontaneous conversational speech, but present evaluation designs continue to rely on known topic boundaries that are no longer well matched to the nature of the materials. We propose a new class of measures for speech retrieval based on manual annotation of points at which a user with specific topical interests would wish replay to begin.



Collaborative Colleagues:
Baolong Liu: colleagues
Douglas W. Oard: colleagues