Movatterモバイル変換

[0]ホーム
URL:

Skip to main content

Advertisement

Springer Nature Link
Log in

Generalised Fuzzy Hidden Markov Models for Speech Recognition

  • Conference paper
  • First Online:

Part of the book series:Lecture Notes in Computer Science ((LNAI,volume 2275))

Included in the following conference series:

Abstract

A generalised fuzzy approach to statistical modelling techniques for speech recognition is proposed in this paper. Fuzzy C-means (FCM) and fuzzy entropy (FE) techniques are combined into a generalised fuzzy technique and applied to hidden Markov models (HMMs). A more robust version of the above fuzzy technique based on the noise clustering (NC) method is also proposed. Experimental results were performed on the TI46 speech data corpus. A significant result for isolatedword recognition performed on a highly confusable vocabulary consisting of the nine English E-set words is that, a 33.8% recognition error rate for the HMM-based system was reduced to 30.5%, 29.9%, 29.8% and 27.8%, respectively, by using the FCM-HMM, the FE-HMM, the NC-FE-HMM, and the NC-FCM-HMM-based systems.

This is a preview of subscription content,log in via an institution to check access.

Access this chapter

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. J. C. Bezdek,Pattern recognition with fuzzy objective function algorithms, Plenum Press, New York and London, 1981.

    MATH  Google Scholar 

  2. H. J. Choi and Y. H. Oh, “Speech recognition using an enhanced FVQ based on codeword dependent distribution normalization and codeword weighting by fuzzy objective function”, inProceedings of the International Conference on Spoken Language Processing (ICSLP), vol. 1, pp. 354–357, 1996.

    Google Scholar 

  3. R. N. Davé, “Characterization and detection of noise in clustering“,Pattern Recognition Lett., vol. 12, no. 11, pp. 657–664, 1991.

    Article  Google Scholar 

  4. R. N. Davé and R. Krishnapuram, “Robust clustering methods: a unified view”,IEEE Trans. Fuzzy Syst., vol. 5, no.2, pp. 270–293.

    Google Scholar 

  5. X. D. Huang, Y. Ariki and M. A. Jack,Hidden Markov models for speech recognition, Edinburgh University Press, 1990.

    Google Scholar 

  6. N. Kasabov, R. Kozma, R. Kilgour, M. Laws, J. Taylor, M. Watts and A. Gray, “Hybrid connectionist-based methods and systems for speech data analysis and phoneme-based speech recognition” inNeuro-Fuzzy Techniques for Intelligent Information Processing, N. Kasabov & R. Kozma, eds., Physica Verlag, 1999.

    Google Scholar 

  7. S. K. Pal and D. D. Majumder, “Fuzzy sets and decision making approaches in vowel and speaker recognition”,IEEE Trans. Syst. Man Cybern., pp. 625–629, 1977.

    Google Scholar 

  8. E. Tsuboka and J. Nakahashi, “On the fuzzy vector quantisation based hidden Markov model”, inProc. Inter. Conf. on Acoustics, Speech & Signal Processing (ICASSP’94), vol. 1, pp. 537–640, 1994.

    Google Scholar 

  9. Dat Tran and Michael Wagner, “Fuzzy entropy clustering”, the FUZZ-IEEE’2000 Conf., vol. 1, pp. 152–158, USA.

    Google Scholar 

  10. Dat Tran and Michael Wagner, “Hidden Markov models using fuzzy estimation”, inProc. EUROSPEECH’99 Conf., vol. 6, pp. 2749–2752, Hungary, 1999.

    Google Scholar 

  11. Dat Tran and Michael Wagner, “Fuzzy hidden Markov models for speech and speaker recognition”, inProc. NAFIPS’99, pp. 426–430, USA, 1999.

    Google Scholar 

  12. L. R. Rabiner and B. H. Juang,Fundamentals of speech recognition, Prentice Hall PTR, USA, 1993.

    Google Scholar 

  13. L. A. Zadeh, “Fuzzy sets”,Inf. Control., vol. 8, no. 1, pp. 338–353, 1965.

    Article MATH MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computing, University of Canberra, 2601, ACT, Australia

    Dat Tran & Michael Wagner

Authors
  1. Dat Tran

    You can also search for this author inPubMed Google Scholar

  2. Michael Wagner

    You can also search for this author inPubMed Google Scholar

Editor information

Editors and Affiliations

  1. Electronics and Communication Sciences Unit, Indian Statistical Institute, 203 B.T. Road, 700108, Calcutta, India

    Nikhil R. Pal

  2. Brain Science Institute, RIKEN, 2-1 Hirosawa, Wako, Japan

    Michio Sugeno

Rights and permissions

Copyright information

© 2002 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Tran, D., Wagner, M. (2002). Generalised Fuzzy Hidden Markov Models for Speech Recognition. In: Pal, N.R., Sugeno, M. (eds) Advances in Soft Computing — AFSS 2002. AFSS 2002. Lecture Notes in Computer Science(), vol 2275. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45631-7_46

Download citation

Publish with us

[8]ページ先頭
©2009-2025 Movatter.jp