Reranking for Sentence Boundary Detection in Conversational Speech

2006 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING, VOLS 1-13(2006)

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摘要
We present a reranking approach to sentence-like unit (SU) boundary detection, one of the EARS metadata extraction tasks. Techniques for generating relatively small n-best lists with high oracle accuracy are presented. For each candidate, features are derived from a range of information sources, including the output of a number of parsers. Our approach yields significant improvements over the best performing system from the NIST RT-04F community evaluation
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关键词
meta data,speech recognition,EARS metadata extraction tasks,NIST RT-04F community evaluation,conversational speech,oracle accuracy,reranking,sentence-like unit boundary detection
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