Show simple item record

dc.contributor.authorCarreira-Perpinan, Miguel A
dc.contributor.authorRenals, Steve
dc.coverage.spatial24en
dc.date.accessioned2006-05-15T12:19:03Z
dc.date.available2006-05-15T12:19:03Z
dc.date.issued1998-12
dc.identifier.citationSpeech Communication (1998) 26, 259-282.en
dc.identifier.issn0167-6393
dc.identifier.urihttp://dx.doi.org/10.1016/S0167-6393(98)00059-4
dc.identifier.urihttp://hdl.handle.net/1842/1072
dc.description.abstractWe consider the problem of obtaining a reduced dimension representation of electropalatographic (EPG) data. An unsupervised learning approach based on latent variable modelling is adopted, in which an underlying lower dimension representation is inferred directly from the data. Several latent variable models are investigated, including factor analysis and the generative topographic mapping (GTM). Experiments were carried out using a subset of the EUR-ACCOR database, and the results indicate that these automatic methods capture important, adaptive structure in the EPG data. Nonlinear latent variable modelling clearly outperforms the investigated linear models in terms of log-likelihood and reconstruction error and suggests a substantially smaller intrinsic dimensionality for the EPG data than that claimed by previous studies. A two-dimensional representation is produced with applications to speech therapy, language learning and articulatory dynamics.en
dc.format.extent574055 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherElsevieren
dc.subjectElectropalatographyen
dc.subjectArticulatory modellingen
dc.subjectData reduction methodsen
dc.subjectDimensionality reductionen
dc.subjectLatent variable modelsen
dc.subjectFinite mixture distributionsen
dc.subjectMixture modelsen
dc.subjectPrincipal component analysisen
dc.subjectFactor analysisen
dc.subjectMixtures of factor analysersen
dc.subjectGeneralised topographic mappingen
dc.subjectMixtures of multivariate Bernoulli distributionsen
dc.titleDimensionality reduction of electropalatographic data using latent variable modelsen
dc.typeArticleen


Files in this item

This item appears in the following Collection(s)

Show simple item record