Continuous speech recognition using multilayer perceptrons with hidden Markov models

This is the authors' abstract. We don't add key points for this paper.

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.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>