A simulation of final stop consonants in speech perception using the bicameral neural network model

Authors:
Michael C. Stinson;Dan Foster
Affiliations:
Department of Computer Science, Central Michigan University, Mt. Pleasant, Michigan;Department of English, University of Winnipeg, Winnipeg, Manitoba, Canada
Venue:
ANSS '90 Proceedings of the 23rd annual symposium on Simulation
Year:
1990

Citing 4
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Abstract

This paper demonstrates the integration of contextual information in a neural network for speech perception. Neural networks have been unable to integrate such information successfully because they cannot implement conditional rule structures. The Bicameral neural network employs an asynchronous controller which allows conditional rules to choose neurons for update rather than updating them randomly. The Bicameral model is applied to the perception of word-final plosives, an ongoing problem for machine recognition of speech.