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


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.


Neural network Genetic algorithm Fuzzy logic Speech recognition Pattern recognition 



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).


  1. 1.
    Rabiner L, Juang BH (1993) Fundamentals of speech recognition. Prentice-Hall, Englewood CliffsGoogle Scholar
  2. 2.
    Hanaki Y, Hashiyama T, Okuma S (1999) Accelerated evolutionary computation using fitness estimation. In: Proceedings of IEEE international conference on systems, man, and cybernetics, vol 1, pp 643–648Google Scholar
  3. 3.
    Lee T, Lo WK, Ching PC, Meng H (2002) Spoken language resources for Cantonese speech processing. Speech Commun 36(3–4):327–342zbMATHCrossRefGoogle Scholar
  4. 4.
    Kosko B (1991) Neural networks and fuzzy system: a dynamical systems approach to machine intelligence. Prentice-Hall, Englewood CliffsGoogle Scholar
  5. 5.
    Haykin S (1999) Neural networks: a comprehensive foundation, 2nd edn. Prentice-Hall, Englewood CliffszbMATHGoogle Scholar
  6. 6.
    Brown M, Harris C (1994) Neuralfuzzy adaptive modeling and control. Prentice-Hall, Englewood CliffsGoogle Scholar
  7. 7.
    Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann ArborGoogle Scholar
  8. 8.
    Pham DT, Karaboga D (2000) Intelligent optimization techniques, genetic algorithms, tabu search, simulated annealing and neural networks. Springer, Berlin Heidelberg New YorkGoogle Scholar
  9. 9.
    Michalewicz Z (1994) Genetic algorithm + data structures = evolution programs, 2nd edn. Springer, Berlin Heidelberg New YorkGoogle Scholar
  10. 10.
    Leung FHF, Lam HK, Ling SH, Tam PKS (2004) Optimal and stable fuzzy controllers for uncertain nonlinear systems based on an improved genetic algorithm. IEEE Trans Ind Electron 51(1):172–182CrossRefGoogle Scholar
  11. 11.
    Zhou YS, Lai LY (2000) Optimal design for fuzzy controllers by genetic algorithms. IEEE Trans Ind Appl 36(1):93–97CrossRefGoogle Scholar
  12. 12.
    Juang CF, Lin JY, Lin CT (2000) Genetic reinforcement learning through symbiotic evolution for fuzzy controller design. IEEE Trans Syst Man Cybern Part B 30(2):290–302CrossRefMathSciNetGoogle Scholar
  13. 13.
    Belarbi K, Titel F (2000) Genetic algorithm for the design of a class of fuzzy controllers: an alternative approach. IEEE Trans Fuzzy Syst 8(4):398–405CrossRefGoogle Scholar
  14. 14.
    Juidette H, Youlal H (2000) Fuzzy dynamic path planning using genetic algorithms. Electron Lett 36(4):374–376CrossRefGoogle Scholar
  15. 15.
    Caponetto R, Fortuna L, Nunnari G, Occhipinti L, Xibilia MG (2000) Soft computing for greenhouse climate control. IEEE Trans Fuzzy Syst 8(6):753–760CrossRefGoogle Scholar
  16. 16.
    Setnes M, Roubos H (2000) GA-fuzzy modeling and classification: complexity and performance. IEEE Trans Fuzzy Syst 8(5):509–522CrossRefGoogle Scholar
  17. 17.
    Davis L (1991) Handbook of genetic algorithms. Van Nostrand Reinhold, New YorkGoogle Scholar
  18. 18.
    Wang X, Elbuluk M (1996) Neural network control of induction machines using genetic algorithm training. In: Conference record of 31st IAS annual meeting, vol 3, pp 1733–1740Google Scholar
  19. 19.
    Srinivas M, Patnaik LM (1994) Genetic algorithms: a survey. IEEE Comput 27(6):17–26Google Scholar
  20. 20.
    De Jong KA (1975) An analysis of the behavior of a class of genetic adaptive systems. PhD thesis, University of MichiganGoogle Scholar
  21. 21.
    Amin S, Fernandez-Villacanas JL (1997) Dynamic local search. In: Proceedings of 2nd international conference on genetic algorithms in engineering systems: innovations and applications, pp 129–132Google Scholar

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

Personalised recommendations