NDLI: Gaussian mixture models with covariances or precisions in shared multiple subspaces

Content Provider	IEEE Xplore Digital Library
Author	Dharanipragada, S. Visweswariah, K.
Copyright Year	2006
Abstract	We introduce a class of Gaussian mixture models (GMMs) in which the covariances or the precisions (inverse covariances) are restricted to lie in subspaces spanned by rank-one symmetric matrices. The rank-one basis are shared between the Gaussians according to a sharing structure. We describe an algorithm for estimating the parameters of the GMM in a maximum likelihood framework given a sharing structure. We employ these models for modeling the observations in the hidden-states of a hidden Markov model based speech recognition system. We show that this class of models provide improvement in accuracy and computational efficiency over well-known covariance modeling techniques such as classical factor analysis, shared factor analysis and maximum likelihood linear transformation based models which are special instances of this class of models. We also investigate different sharing mechanisms. We show that for the same number of parameters, modeling precisions leads to better performance when compared to modeling covariances. Modeling precisions also gives a distinct advantage in computational and memory requirements
Sponsorship	IEEE Signal Processing Society
Starting Page	1255
Ending Page	1266
Page Count	12
File Size	371267
File Format	PDF
ISSN	15587916
Volume Number	14
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-07-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Maximum likelihood estimation Speech recognition Symmetric matrices Covariance matrix Speech analysis Subspace constraints Parameter estimation Computational efficiency Density functional theory speech recognition Covariance matrices density functions EM algorithm factor analysis Gaussian mixture models (GMMs)
Content Type	Text
Resource Type	Article
Subject	Acoustics and Ultrasonics Electrical and Electronic Engineering

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