Maximum likelihood estimation for multivariate mixture observations of Markov chins
IEEE Transactions on Information Theory
Factorial Hidden Markov Models
Machine Learning - Special issue on learning with probabilistic representations
Speech recognition: theory and C++ implementation
Speech recognition: theory and C++ implementation
A minimum description length framework for unsupervised learning
A minimum description length framework for unsupervised learning
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We present a factorial representation of Gaussian mixture models for observation densities in hidden Markov models (HMMs), which uses the factorial learning in the HMM framework. We derive the reestimation formulas for estimating the factorized parameters by the Expectation Maximization (EM) algorithm and propose a novel method for initializing them. To compare the performances of the proposed models with that of the factorial hidden Markov models and HMMs, we have carried out extensive experiments which show that this modelling approach is effective and robust.