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dc.contributor.authorDielmann, Alfred
dc.contributor.authorRenals, Steve
dc.date.accessioned2006-05-12T12:50:48Z
dc.date.available2006-05-12T12:50:48Z
dc.date.issued2005
dc.identifier.citationIn S. Bengio and H. Bourlard, editors, Proc. Multimodal Interaction and Related Machine Learning Algorithms Workshop (MLMI-04), pages 76-86. Springer, 2005.en
dc.identifier.urihttp://hdl.handle.net/1842/1035
dc.description.abstractConsonant duration is influenced by a number of linguistic factors such as the consonant s identity, within-word position, stress level of the previous and following vowels, phrasal position of the word containing the target consonant, its syllabic position, identity of the previous and following segments. In our work, consonant duration is predicted from a Bayesian belief network (BN) consisting of discrete nodes for the linguistic factors and a single continuous node for the consonant s duration. Interactions between factors are represented as conditional dependency arcs in this graphical model. Given the parameters of the belief network, the duration of each consonant in the test set is then predicted as the value with the maximum probability. We compare the results of the belief network model with those of sums-of-products (SoP) and classification and regression tree (CART) models using the same data. In terms of RMS error, our BN model performs better than both CART and SoP models. In terms of the correlation coefficient, our BN model performs better than SoP model, and no worse than CART model. In addition, the Bayesian model reliably predicts consonant duration in cases of missing or hidden linguistic factors.en
dc.format.extent141841 bytes
dc.format.extent92152 bytes
dc.format.mimetypeapplication/octet-stream
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherSpringeren
dc.subjectspeechen
dc.subjectBayesian belief networken
dc.subjectconsonant durationen
dc.subjectclassification and regression treeen
dc.subjectsums-of-productsen
dc.titleMultistream dynamic Bayesian network for meeting segmentationen
dc.typeConference Paperen


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