A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition
Lawrence R. Rabiner
Extension to Hidden Markov Models
"So far we have considered Markov models in which each state corresponded to an observable (physical) event. This model is too restrictive to be applicable to many problems of interest. In this section we extend the concept of Markov models to include the case where the observable is a probabilistic function of the state - i.e., the resulting model (which is called a hidden Markov model) is a doubly embedded stochastic process that is not observable (it is hidden), but can only be observed through another set of stochastic processes that produce the sequence of observations."