NDLI: Covariance and precision modeling in shared multiple subspaces

Content Provider	IEEE Xplore Digital Library
Author	Dharanipragada, S. Visweswariah, K.
Copyright Year	2003
Description	Author affiliation: IBM T. J. Watson Res. Center, Yorktown Heights, NY, USA (Dharanipragada, S.; Visweswariah, K.)
Abstract	We introduce a class of Gaussian mixture models for HMM states in continuous speech recognition. In these models, the covariances or the precisions (inverse covariances) are restricted to lie in subspaces spanned by rank-one symmetric matrices. In both cases, the rank-one matrices are shared across classes of Gaussians. We show that, for the same number of parameters, modeling precisions leads to better performance when compared to modeling covariances. Modeling precisions however gives a distinct advantage in computational and memory requirements. We also show that this class of models provides improvement in accuracy (for the same number of parameters) over classical factor analysed models and the recently proposed EMLLT (extended maximum likelihood linear transform) models which are special instances of this class of models.
Sponsorship	IEEE Signal Process, Soc
File Size	326992
File Format	PDF
ISBN	0780376633
ISSN	15206149
DOI	10.1109/ICASSP.2003.1198916
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-04-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Covariance matrix Symmetric matrices Parameter estimation Maximum likelihood estimation Speech recognition Computational complexity Robustness Unsolicited electronic mail Closed-form solution
Content Type	Text
Resource Type	Article

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