Traditionally, in speech recognition, the hidden Markov model state emission probability
distributions are usually associated to continuous random variables, by using Gaussian
mixtures. Thus, the inter-feature dependence is not accurately modeled by the covariance matrix,
since it only considers pairs of variables. The mixture is the part of the model which usually
captures this information, but this is done in a loose and inefficient way. Graphical models provide
a precise and simple mechanism to model the dependencies among two or more variables.
We propose the use of discrete random variables as observations and graphical models to extract
the internal dependence structure in the feature vectors. A method to estimate a graphical model
with a constrained number of dependencies is shown in this chapter, which is a special kind of
Bayesian network. Experimental results show that this method can be considered robust as compared to standard baseline systems.
Keywords: Graphical Models, Bayesian networks, Maximum Likelihood, Expectation maximization
