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Please use this identifier to cite or link to this item: http://hdl.handle.net/1842/1232

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Title: Automatic detection of discourse structure for speech recognition and understanding.
Authors: Jurafsky, Daniel
Bates, Rebecca
Coccaro, Noah
Martin, Rachel
Meteer, Marie
Ries, Klaus
Shriberg, Elizabeth
Stolcke, Andreas
Taylor, Paul A
Van Ess-Dykema, Carol
Issue Date: 1997
Citation: In 1997 IEEEWorkshop on Speech Recognition and Understanding,, Santa Barbara, 1997.
Publisher: IEEE
Abstract: We describe a new approach for statistical modeling and detection of discourse structure for natural conversational speech. Our model is based on 42 ‘Dialog Acts’ (DAs), (question, answer, backchannel, agreement, disagreement, apology, etc). We labeled 1155 conversations from the Switchboard (SWBD) database (Godfrey et al. 1992) of human-to-human telephone conversations with these 42 types and trained a Dialog Act detector based on three distinct knowledge sources: sequences of words which characterize a dialog act, prosodic features which characterize a dialog act, and a statistical Discourse Grammar. Our combined detector, although still in preliminary stages, already achieves a 65% Dialog Act detection rate based on acoustic waveforms, and 72% accuracy based on word transcripts. Using this detector to switch among the 42 Dialog- Act-Specific trigram LMs also gave us an encouraging but not statistically significant reduction in SWBD word error.
URI: http://hdl.handle.net/1842/1232
Appears in Collections:CSTR publications

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