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

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.

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.7 Natural Language Processing
          Subjects: Speech recognition and synthesis

  J. Computer Applications
  J.5 ARTS AND HUMANITIES
      Subjects: Linguistics

General Terms:
Design, Theory