Jia, L and Yu, K and Xu, B (2011) Structured precision modelling with Cholesky basis superposition for speech recognition. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. pp. 5168-5171. ISSN 1520-6149Full text not available from this repository.
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. © 2011 IEEE.
|Uncontrolled Keywords:||Cholesky square root inverse covariance modeling precision modelling|
|Divisions:||Div F > Machine Intelligence|
|Depositing User:||Unnamed user with email firstname.lastname@example.org|
|Date Deposited:||16 Jul 2015 14:06|
|Last Modified:||31 Jul 2015 23:27|