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dc.contributor.authorDielmann, Alfred
dc.contributor.authorRenals, Steve
dc.date.accessioned2007-09-19T12:42:01Z
dc.date.available2007-09-19T12:42:01Z
dc.date.issued2007
dc.identifier.citationA. Dielmann and S. Renals. Automatic dialogue act recognition using a dynamic Bayesian network. In S. Renals, S. Bengio, and J. Fiscus, editors, Proc. Multimodal Interaction and Related Machine Learning Algorithms Workshop (MLMI-06), pages 178-189. Springer, 2007.en
dc.identifier.urihttp://hdl.handle.net/1842/2004
dc.description.abstractWe propose a joint segmentation and classification approach for the dialogue act recognition task on natural multi-party meetings (ICSI Meeting Corpus). Five broad DA categories are automatically recognised using a generative Dynamic Bayesian Network based infrastructure. Prosodic features and a switching graphical model are used to estimate DA boundaries, in conjunction with a factored language model which is used to relate words and DA categories. This easily generalizable and extensible system promotes a rational approach to the joint DA segmentation and recognition task, and is capable of good recognition performance.en
dc.format.extent94479 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.subjectspeech technologyen
dc.titleAutomatic dialogue act recognition using a dynamic Bayesian networken
dc.typeConference Paperen


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