Hidden Markov Models with Factored Gaussian Mixtures Densities

• 出 处: The Journal of China Universities of Posts and Telecommunica
• 发表日期: 2004年03期
• 关键词: Mixture tongue speakers logP prior variance cisco Toronto

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. We conduct several experiments to compare the performance of this model structure with Factorial Hidden Markov Models(FHMMs) and HMMs, some conclusions and promising empirical results are presented.

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. We conduct several experiments to compare the performance of this model structure with Factorial Hidden Markov Models (FHMMs) and HMMs, some conclusions and promising results are presented.