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A self-organizing neural network approach for the acquisition of phonetic categories

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Artificial Neural Networks — ICANN 96 (ICANN 1996)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1112))

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Abstract

We present a neural network approach to the process of acquisition of phonetic categories in infants. In our approach we investigate the question to what extend the development of phonetic categories can be described by a self-organizing process. Simulation results show that with digitized speech as input, the network is able to learn representations of the vowel categories in the input set.

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References

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Authors and Affiliations

  1. Max Planck Institute for Psycholinguistics, Postbus 310, 6500, AH Nijmegen, The Netherlands

    Kay Behnke & Peter Wittenburg

Authors
  1. Kay Behnke
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  2. Peter Wittenburg
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Editor information

Christoph von der Malsburg Werner von Seelen Jan C. Vorbrüggen Bernhard Sendhoff

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© 1996 Springer-Verlag Berlin Heidelberg

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Behnke, K., Wittenburg, P. (1996). A self-organizing neural network approach for the acquisition of phonetic categories. In: von der Malsburg, C., von Seelen, W., Vorbrüggen, J.C., Sendhoff, B. (eds) Artificial Neural Networks — ICANN 96. ICANN 1996. Lecture Notes in Computer Science, vol 1112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61510-5_148

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  • DOI: https://doi.org/10.1007/3-540-61510-5_148

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61510-1

  • Online ISBN: 978-3-540-68684-2

  • eBook Packages: Springer Book Archive

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