Connectionist Approaches to the Use of Markov Models for Speech Recognition

Previous work has shown the ability of Multilayer Perceptrons (MLPs) to estimate emission probabilities for Hidden Markov Mod(cid:173) els (HMMs). The advantages of a speech recognition system incor(cid:173) porating both MLPs and HMMs are the best discrimination and the ability to incorporate multiple sources of evidence (features, temporal context) without restrictive assumptions of distributions or statistical independence. This paper presents results on the speaker-dependent portion of DARPA's English language Resource Management database. Results support the previously reported utility of MLP probability estimation for continuous speech recog(cid:173) nition. An additional approach we are pursuing is to use MLPs as nonlinear predictors for autoregressive HMMs.