Estimation of global posteriors and forward-backward training of hybrid HMM/ANN systems

Abstract

The results of our research presented in this paper is two-fold.First, an estimation of global posteriors is formalized in the framework of hybrid HMM/ANN systems.It is shown that hybrid HMM/ANN systems, in which the ANN part estimates local posteriors, can be used to modelize global model posteriors.This formalization provides us with a clear theory in which both REMAP and \classical" Viterbi trained hybrid systems are uni ed.Second, a new forward-backward training of hybrid HMM/ANN systems is derived from the previous formulation.Comparisons of performance between Viterbi and forward-backward hybrid systems are presented and discussed.