Information Services banner Edinburgh Research Archive The University of Edinburgh crest

Edinburgh Research Archive >
Centre for Speech Technology Research >
CSTR publications >

Please use this identifier to cite or link to this item:

This item has been viewed 10 times in the last year. View Statistics

Files in This Item:

File Description SizeFormat
SC_carreira.pdf1.59 MBAdobe PDFView/Open
Title: Dimensionality reduction of electropalatographic data using latent variable models
Authors: Carreira-Perpinan, Miguel A
Renals, Steve
Issue Date: Dec-1998
Citation: Speech Communication (1998) 26, 259-282.
Publisher: Elsevier
Abstract: We 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.
Keywords: Electropalatography
Articulatory modelling
Data reduction methods
Dimensionality reduction
Latent variable models
Finite mixture distributions
Mixture models
Principal component analysis
Factor analysis
Mixtures of factor analysers
Generalised topographic mapping
Mixtures of multivariate Bernoulli distributions
ISSN: 0167-6393
Appears in Collections:CSTR publications

Items in ERA are protected by copyright, with all rights reserved, unless otherwise indicated.


Valid XHTML 1.0! Unless explicitly stated otherwise, all material is copyright © The University of Edinburgh 2013, and/or the original authors. Privacy and Cookies Policy