Context-Dependent Multiple Distribution Phonetic Modeling with MLPs

A number of hybrid multilayer perceptron (MLP)/hidden Markov model (HMM: ) speech recognition systems have been developed in recent years (Morgan and Bourlard. 1990). In this paper. we present a new MLP architecture and training algorithm which allows the modeling of context-dependent phonetic classes in a hybrid MLP/HMM: framework. The new training procedure smooths MLPs trained at different degrees of context dependence in order to obtain a robust estimate of the cootext-dependent probabilities. Tests with the DARPA Resomce Management database have shown substantial advantages of the context-dependent MLPs over earlier cootext(cid: 173) independent MLPs. and have shown substantial advantages of this hybrid approach over a pure HMM approach.

Authors

• Michael Cohen
• Horacio Franco
• Nelson Morgan
• David Rumelhart
• Victor Abrash

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