Abstract
In this paper we present a hybrid multilayer perceptron (MLP)/hidden Markov model (HMM) speaker-independent continuous-speech recognition system, in which the advantages of both approaches are combined by using MLPs to estimate the state-dependent observation probabilities of an HMM. New MLP architectures and training procedures are presented which allow the modeling of multiple distributions for phonetic classes and context-dependent phonetic classes. Comparisons with a pure HMM system illustrate advantages of the hybrid approach both in terms of recognition accuracy and number of parameters required.
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