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dc.contributor.authorLincoln, Michael
dc.contributor.authorCox, Stephen
dc.contributor.authorRingland, Simon
dc.coverage.spatial4en
dc.date.accessioned2006-05-11T16:51:58Z
dc.date.available2006-05-11T16:51:58Z
dc.date.issued1998-12
dc.identifier.citationIn ICSLP-1998, paper 0465.en
dc.identifier.urihttp://www.isca-speech.org/archive/icslp_1998/index.html
dc.identifier.urihttp://hdl.handle.net/1842/1008
dc.description.abstractThe ability to automatically identify a speaker's accent would be very useful for a speech recognition system as it would enable the system to use both a pronunciation dictionary and speech models specific to the accent, techniques which have been shown to improve accuracy. Here, we describe some experiments in unsupervised accent classification. Two techniques have been investigated to classify British- and American-accented speech: an acoustic approach, in which we analyse the pattern of usage of the distributions in the recogniser by a speaker to decide on his most probable accent, and a high-level approach in which we use a phonotactic model for classification of the accent. Results show that both techniques give excellent performance on this task which is maintained when testing is done on data from an independent dataset.en
dc.format.extent225848 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherInternational Speech Communication Associationen
dc.titleA Comparison of Two Unsupervised Approaches to Accent Identificationen
dc.typeConference Paperen


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