Continuous speech recognition using multilayer perceptrons with hidden Markov models

A phoneme based, speaker-dependent continuous-speech recognition system embedding a multilayer perceptron (MLP) (i.e. a feedforward artificial neural network) into a hidden Markov model (HMM) approach is described. Contextual information from a sliding window on the input frames is used to improve frame or phoneme classification performance over the corresponding performance for simple maximum-likelihood probabilities, or even maximum a posteriori (MAP) probabilities which are estimated without the benefit of context. Performance for a simple discrete density HMM system appears to be somewhat better when MLP methods are used to estimate the probabilities.>