Structural Bayesian Linear Regression for Hidden Markov Models

Linear regression for Hidden Markov Model (HMM) parameters is widely used for the adaptive training of time series pattern analysis especially for speech processing. The regression parameters are usually shared among sets of Gaussians in HMMs where the Gaussian clusters are represented by a tree. Th...

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Bibliographic Details
Published in:Journal of signal processing systems 2014-03, Vol.74 (3), p.341-358
Main Authors: Watanabe, Shinji, Nakamura, Atsushi, Juang, Biing-Hwang (Fred)
Format: Article
Language:English
Subjects:
Circuits and Systems
Computer Imaging
Electrical Engineering
Engineering
Image Processing and Computer Vision
Pattern Recognition
Pattern Recognition and Graphics
Signal,Image and Speech Processing
Vision
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Summary:Linear regression for Hidden Markov Model (HMM) parameters is widely used for the adaptive training of time series pattern analysis especially for speech processing. The regression parameters are usually shared among sets of Gaussians in HMMs where the Gaussian clusters are represented by a tree. This paper realizes a fully Bayesian treatment of linear regression for HMMs considering this regression tree structure by using variational techniques. This paper analytically derives the variational lower bound of the marginalized log-likelihood of the linear regression. By using the variational lower bound as an objective function, we can algorithmically optimize the tree structure and hyper-parameters of the linear regression rather than heuristically tweaking them as tuning parameters. Experiments on large vocabulary continuous speech recognition confirm the generalizability of the proposed approach, especially when the amount of adaptation data is limited.
ISSN:1939-8018
1939-8115
DOI:10.1007/s11265-013-0785-8