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Modeling human word recognition with sequences of artificial neurons

  • P. Wittenburg
  • D. van Kuijk
  • T. Dijkstra
Oral Presentations: Cognitive Science and AI Cognitive Science and AI II: Symbolic Processing
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1112)

Abstract

A new psycholinguistically motivated and neural network based model of human word recognition is presented. In contrast to earlier models it uses real speech as input. At the word layer acoustical and temporal information is stored by sequences of connected sensory neurons which pass on sensor potentials to a word neuron. In experiments with a small lexicon which includes groups of very similar word forms, the model meets high standards with respect to word recognition and simulates a number of wellknown psycholinguistical effects.

Keywords

Sensor Neuron Word Recognition Speech Signal Automatic Speech Recognition Automatic Speech Recognition System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • P. Wittenburg
    • 1
  • D. van Kuijk
    • 1
  • T. Dijkstra
    • 2
  1. 1.Max-Planck-Institute for PsycholinguisticsNijmegen
  2. 2.NICIUniversity of NijmegenNijmegenThe Netherlands

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