A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
An Introduction to Variational Methods for Graphical Models
Machine Learning
Improved Bayesian Learning of Hidden Markov Models for Speaker Adaptation
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 2 - Volume 2
Machine Learning: Discriminative and Generative (Kluwer International Series in Engineering and Computer Science)
The Journal of Machine Learning Research
On transforming statistical models for non-frontal face verification
Pattern Recognition
Adaptation of pitch and spectrum for HMM-based speech synthesis using MLLR
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 200. on IEEE International Conference - Volume 02
IEEE Transactions on Audio, Speech, and Language Processing
Unsupervised Activity Recognition with User's Physical Characteristics Data
ISWC '11 Proceedings of the 2011 15th Annual International Symposium on Wearable Computers
Inferring parameters and structure of latent variable models by variational bayes
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Discriminative Training for Large-Vocabulary Speech Recognition Using Minimum Classification Error
IEEE Transactions on Audio, Speech, and Language Processing
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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.