NDLI: Structured precision modelling with Cholesky Basis Superposition for speech recognition

Content Provider	IEEE Xplore Digital Library
Author	Lei Jia Kai Yu Bo Xu
Copyright Year	2011
Description	Author affiliation: Digital Media Content Technology Center, Institute of Automation, C.A.S., 100190, Beijing, China (Lei Jia; Bo Xu) \|\| Cambridge University Engineering Department, Trumpington Street, CB2 1PZ, UK (Kai Yu)
Abstract	Structured precision modelling is an important approach to improve the intra-frame correlation modelling of the standard HMM, where Gaussian mixture model with diagonal covariance are used. Previous work has all been focused on direct structured representation of the precision matrices. In this paper, a new framework is proposed, where the structure of the Cholesky square root of the precision matrix is investigated, referred to as Cholesky Basis Superposition (CBS). Each Cholesky matrix associated with a particular Gaussian distribution is represented as a linear combination of a set of Gaussian independent basis upper-triangular matrices. Efficient optimization methods are derived for both combination weights and basis matrices. Experiments on a Chinese dictation task showed that the proposed approach can significantly outperformed the direct structured precision modelling with similar number of parameters as well as full covariance modelling.
Starting Page	5168
Ending Page	5171
File Size	115551
Page Count	4
File Format	PDF
ISBN	9781457705380
ISSN	15206149
e-ISBN	9781457705397
e-ISBN	9781457705373
DOI	10.1109/ICASSP.2011.5947521
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-05-22
Publisher Place	Czech Republic
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Covariance matrix Mathematical model Unsolicited electronic mail Equations Symmetric matrices Optimization precision modelling inverse covariance modeling Cholesky square root
Content Type	Text
Resource Type	Article

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