Generating Individualized Utterances for Dialogue Systems

dc.contributor.authorMairesse, Francoisde
dc.contributor.authorWalker, Marlynde
dc.date.accessioned2005-06-15T08:12:59Z
dc.date.available2005-06-15T08:12:59Z
dc.date.created2005de
dc.date.issued2005-06-13de
dc.description.abstractOne of the most robust findings of studies of human-human dialogue is that people adapt their utterances to their conversational partners. However, spoken language generators are limited in their ability to adapt to individual users. While statistical models of language generation have the potential for individual adaptation, we know of no experiments showing this. In this paper, we utilize one statistical method, boosting, to train a spoken language generator for individual users. We show that individualized models perform better than models based on sets of users, and describe differences in the learned individual models arising from the linguistic preferences of users.en
dc.format.extent151978 bytes
dc.format.extent1485360 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/postscript
dc.identifier.urihttp://hdl.handle.net/2003/21467
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-14199
dc.language.isoen
dc.publisherUniversity of Dortmunden
dc.relation.ispartofSymposium on Dialogue Modelling and Generation at the 15th Annual meeting of the Society for Text and Discourse. Amsterdam, The Netherlandsen
dc.subject.ddc430de
dc.titleGenerating Individualized Utterances for Dialogue Systemsen
dc.typeTextde
dc.type.publicationtypeconferenceObject
dcterms.accessRightsopen access

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