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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 9 of 9 1 Symbolic phonetic features for modeling of pronunciation variation Ostendorf, Mari; Bates, Rebecca A.; Wright, Richard A. In: Speech communication. - Amsterdam [u.a.] : Elsevier 49 (2007) 2, 83-97 BLLDB OLC Linguistik Show details 2 Speaker dynamics as a source of pronunciation variability for continuous speech recognition models Bates, Rebecca Anne. - 2004 BASE Show details 3 Articles - Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational Speech Stolcke, Andreas; Ries, Klaus; Coccaro, Noah... In: Computational linguistics. - Cambridge, Mass. : MIT Press 26 (2000) 3, 339-374 OLC Linguistik Show details 4 Dialogue act modeling for automatic tagging and recognition of conversational speech Stolcke, Andreas; Coccaro, Noah; Bates, Rebecca... In: Computational linguistics. - Cambridge, Mass. : MIT Press 26 (2000) 3, 339-373 BLLDB Show details 5 Dialogue act modeling for automatic tagging and recognition of conversational speech Stolcke, Andreas; Coccaro, Noah; Bates, Rebecca; Taylor, Paul; Van Ess-Dykema, Carol; Ries, Klaus; Shriberg, Elizabeth; Jurafsky, Daniel; Martin, Rachel; Meteer, Marie. - : MIT Press, 2000 Abstract: We describe a statistical approach for modeling dialogue acts in conversational speech, i.e., speech-act-like units such as STATEMENT, QUESTION, BACKCHANNEL, AGREEMENT, DISAGREEMENT, and APOLOGY. Our model detects and predicts dialogue acts based on lexical, collocational, and prosodic cues, as well as on the discourse coherence of the dialogue act sequence. The dialogue model is based on treating the discourse structure of a conversation as a hidden Markov model and the individual dialogue acts as observations emanating from the model states. Constraints on the likely sequence of dialogue acts are modeled via a dialogue act n-gram. The statistical dialogue grammar is combined with word n-grams, decision trees, and neural networks modeling the idiosyncratic lexical and prosodic manifestations of each dialogue act. We develop a probabilistic integration of speech recognition with dialogue modeling, to improve both speech recognition and dialogue act classification accuracy. Models are trained and evaluated using a large hand-labeled database of 1,155 conversations from the Switchboard corpus of spontaneous human-to-human telephone speech. We achieved good dialogue act labeling accuracy (65% based on errorful, automatically recognized words and prosody, and 71% based on word transcripts, compared to a chance baseline accuracy of 35% and human accuracy of 84%) and a small reduction in word recognition error. URL: http://hdl.handle.net/1842/975 https://doi.org/10.1162/089120100561737 BASE Hide details 6 Can prosody aid the automatic classification of dialog acts in conversational speech? Shriberg, Elizabeth E.; Stolcke, Andreas; Jurafsky, Dan... In: Language and speech. - London [u.a.] : Sage Publ. 41 (1998) 3-4, 443-492 BLLDB Show details 7 Can Prosody Aid the Automatic Classification of Dialog Acts in Conversational Speech? Shriberg, Elizabeth; Bates, Rebecca; Stolcke, Andreas... In: Language and speech. - London [u.a.] : Sage Publ. 41 (1998) 3-4, 443-492 OLC Linguistik Show details 8 Can Prosody Aid the Automatic Classification of Dialog Acts in Conversational Speech? Shriberg, Elizabeth; Bates, Rebecca; Taylor, Paul. - : Kingston Press Services Ltd., 1998 BASE Show details 9 Automatic detection of discourse structure for speech recognition and understanding. Jurafsky, Daniel; Bates, Rebecca; Coccaro, Noah. - : IEEE, 1997 BASE Show details

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