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Neural Computing and Applications

, Volume 16, Issue 4–5, pp 419–431 | Cite as

Application of a modified neural fuzzy network and an improved genetic algorithm to speech recognition

  • K. F. Leung
  • F. H. F. Leung
  • H. K. Lam
  • S. H. LingEmail author
Original Article

Abstract

This paper presents the recognition of speech commands using a modified neural fuzzy network (NFN). By introducing associative memory (the tuner NFN) into the classification process (the classifier NFN), the network parameters could be made adaptive to changing input data. Then, the search space of the classification network could be enlarged by a single network. To train the parameters of the modified NFN, an improved genetic algorithm is proposed. As an application example, the proposed speech recognition approach is implemented in an eBook experimentally to illustrate the design and its merits.

Keywords

Neural network Genetic algorithm Fuzzy logic Speech recognition Pattern recognition 

Notes

Acknowledgments

The work described in this paper was fully supported by a grant from the Centre for Multimedia Signal Processing, The Hong Kong Polytechnic University (Project No. A432).

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Copyright information

© Springer-Verlag London Limited 2006

Authors and Affiliations

  • K. F. Leung
    • 1
  • F. H. F. Leung
    • 1
  • H. K. Lam
    • 2
  • S. H. Ling
    • 1
    Email author
  1. 1.Centre for Multimedia Signal Processing, Department of Electronic and Information EngineeringThe Hong Kong Polytechnic UniversityHong KongChina
  2. 2.Division of EngineeringKing’s College LondonLondonUK

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