Some properties of continuous hidden Markov model representations.

Many signals can be modeled as probabilistic functions of Markov chains in which the observed signal is a random vector whose proability density function (pdf) depends on the current state of an underlying Markov chain. Such models are called hidden Markov models (HMM) and are useful representations for speech signals in terms of some convenient observations (e.g., cepstral coefficients, pseudo log area ratios, etc.). One method of estimating parameters of HMM's is the well-known Baum-Welch reestimation method. For continuous pdf's the method was known to work only for elliptically symmetric densities. We have recently shown that the method can be generalized to handle mixtures of elliptically symmetric pdf's.