Comparison between two hybrid HMM/MLP approaches in speech recognition

We present and compare two different hybrid HMM/MLP approaches. The first one uses MLPs as labelers coupled with a discrete HMM while the second one takes advantage of the ability of MLPs trained as classifiers to estimate a posteriori probabilities. Both approaches bring sensible improvement compared with classical methods since they rid the system of some restrictive hypotheses inherent of pure HMM design (no time correlation between successive acoustic vectors, hypothesis on the probability distributions...). Our experiments have been achieved in order to provide quite fair comparisons. This implied that we used standard environment namely, standard software, standard databases including common training and test sets