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We extend our previously proposed quasi-Bayes adaptive learning framework to cope with the correlated continuous density hidden Markov models with Gaussian mixture state observation densities in which all mean vectors are assumed to be correlated and have a joint prior distribution. A successive approximation algorithm is proposed to implement the(More)
We introduce a new Bayesian predictive classification (BPC) approach to robust speech recognition and apply the BPC framework to Gaussian mixture continuous density hidden Markov model based speech recognition. We propose and focus on one of the approximate BPC approach called quasi-Bayesian predictive classification (QBPC). In comparison with the standard(More)
In this paper a theoretical framework for Bayesian adaptive learning of discrete HMM and semi-continuous one with Gaussian mixture state observation densities is presented. Corresponding to the well-known Baum-Welch and segmental k-means algorithms respectively for HMM training, formulations of MAP (maximum a posteriori) and segmental MAP estimation of HMM(More)
Invited Paper Recent advances in automatic speech recognition are accomplished by designing a plug-in maximum a posteriori decision rule such that the forms of the acoustic and language model distributions are specified and the parameters of the assumed distributions are estimated from a collection of speech and language training corpora. Maximum-likelihood(More)