Close-Class-Set Discrimination Method for Recognition of Stop_Consonant-Vowel Utterances Using Support Vector Machines | SpringerLink
Skip to main content
Springer Nature Link
Log in

Close-Class-Set Discrimination Method for Recognition of Stop_Consonant-Vowel Utterances Using Support Vector Machines

  • Conference paper
  • First Online:
Artificial Neural Networks — ICANN 2001 (ICANN 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2130))

Included in the following conference series:

  • 3471 Accesses

Abstract

5In conventional approaches for multi-class pattern recognition using Support Vector Machines (SVMs), each class is discriminated against all the other classes to build an SVM for that class. We propose a close-class-set discrimination method suitable for large class set pattern recognition problems. The proposed method is demonstrated for recognition of isolated utterances belonging to 80 Stop_Consonant-Vowel (SCV) classes. In this method, an SVM is built for each SCV class by discriminating that class against only 10 classes close to it phonetically.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. C.J.C. Burges, “A tutorial on support vector machines for pattern recognition,” Data Mining and Knowledge Discovery, vol. 2, no. 2, pp. 121–167, 1998.

    Article  Google Scholar 

  2. V.N. Vapnik, The Nature of Statistical Learning Theory, Springer, 2000.

    Google Scholar 

  3. J. Weston and C. Watkins, “Multi-class support vector machines,” Tech. Rep. CSD-TR-98-04, Royal Holloway, University of London, May 1998.

    Google Scholar 

  4. P. Clarkson and P.J. Moreno, “On the use of support vector machines for phonetic classification,” in ICASSP, Mar. 1999, pp. 585–588.

    Google Scholar 

  5. P. Niyogi, C. Burges, and P. Ramesh, “Distinctive feature detection using support vector machines,” in ICASSP, Mar. 1999, pp. 425–428.

    Google Scholar 

  6. C. Chandra Sekhar, Neural Network Models for Recognition of Stop Consonant-Vowel (SCV) Segments in Continuous Speech, Ph.D. thesis, Indian Institute of Technology, Madras, 1996.

    Google Scholar 

  7. J.Y. Siva Rama Krishna Rao, C.Chandra Sekhar, and B. Yegnanarayana, “Neural networks based approach for detection of vowel onset points,” in International Conference on Advances in Pattern Recognition and Digital Techniques, Calcutta, Dec. 1999, pp. 316–320.

    Google Scholar 

  8. L. R. Rabiner and B. H. Juang, Fundamentals of Speech Recognition, Prentice-Hall, Englewood Cliffs, 1993.

    Google Scholar 

  9. R. Collobert and S. Bengio, “Support vector machines for large-scale regression problems,” Tech. Rep. IDIAP-RR-00-17, IDIAP, Switzerland, 2000.

    Google Scholar 

  10. T.S. Jaakkola and D. Haussler, “Exploiting generative models in discriminative classifiers,” in Advances in Neural Information Processing Systems, M.S. Kearns, S.A. Solla, and D.A. Cohn, Eds., vol. 11. MIT Press, 1998.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. CIAIR, Itakura Laboratory, Dept. of Information Electronics, Graduate School of Engineering Nagoya University, Nagoya, 4648603, Japan

    Chellu Chandra Sekhar, Kazuya Takeda & Fumitada Itakura

Authors
  1. Chellu Chandra Sekhar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kazuya Takeda
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fumitada Itakura
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Dept. of Mecidal Cybernetics and Artificial Intelligence, University of Vienna, Freyung 6/2, 1010, Vienna, Austria

    Georg Dorffner

  2. Institute for Computer Aided Automation Pattern Recognition and Image Processing Group, Technical University of Vienna, Favoritenstr. 9/1832, 1040, Vienna, Austria

    Horst Bischof

  3. Institut für Statistik, Wirtschaftsuniversität Wien, Augasse 2-6, 1090, Wien, Austria

    Kurt Hornik

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Sekhar, C.C., Takeda, K., Itakura, F. (2001). Close-Class-Set Discrimination Method for Recognition of Stop_Consonant-Vowel Utterances Using Support Vector Machines. In: Dorffner, G., Bischof, H., Hornik, K. (eds) Artificial Neural Networks — ICANN 2001. ICANN 2001. Lecture Notes in Computer Science, vol 2130. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44668-0_56

Download citation

  • DOI: https://doi.org/10.1007/3-540-44668-0_56

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42486-4

  • Online ISBN: 978-3-540-44668-2

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics