Advertisement

Text-Independent Speaker Identification Using VQ-HMM Model Based Multiple Classifier System

  • Ali Zulfiqar
  • Aslam Muhammad
  • Ana Maria Martinez-Enriquez
  • G. Escalada-Imaz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6438)

Abstract

Every feature extraction and modeling technique of voice/speech is not suitable in all type of environments. In many real life applications, it is not possible to use all type of feature extraction and modeling techniques to design a single classifier for speaker identification tasks because it will make the system complex. So instead of exploring more techniques or making the system complex it is more reasonable to develop the classifier by using existing techniques and then combine them by using different combination techniques to enhance the performance of the system. Thus, this paper describes the design and implementation of a VQ-HMM based Multiple Classifier System by using different combination techniques. The results show that the developed system by using confusion matrix significantly improve the identification rate.

Keywords

Speaker identification classifier combination HMM VQ MFCC LPC 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Furui, S.: Recent Advances in Speaker Recognition. Pattern Recognition Letter 8(9), 859–872 (1997)CrossRefGoogle Scholar
  2. 2.
    Chen, K., Wang, L., Chi, H.: Methods of Combining Multiple Classifiers with Different Features and Their Application to Text-independent Speaker Identification. International Journal of Pattern Recognition and Artificial Intelligence 11(3), 417–445 (1997)CrossRefGoogle Scholar
  3. 3.
    Reynolds, D.A.: An Overview of Automatic Speaker Recognition Technology. Proc. IEEE 4, 4072–4075 (2002)Google Scholar
  4. 4.
    Godino-Llorente, J.I., Gómez-Vilda, P., Sáenz-Lechón, N., Velasco, M.B., Cruz-Roldán, F., Ballester, M.A.F.: Discriminative Methods for the Detection of Voice Disorder. In: A ISCA Tutorial and Research Workshop on Non-Linear Speech Processing, The COST-277 Workshop (2005)Google Scholar
  5. 5.
    Xugang, L., Jianwu, D.: An investigation of Dependencies between Frequency Components ans Speaker Characteristics for Text-independent Speaker Identification. Speech Communication 2007 50(4), 312–322 (2007)Google Scholar
  6. 6.
    Huang, X.D., Ariki, Y., Jack, M.A.: Hidden Markov Model for Speech Recognition. Edinburgh University Press, Edinburgh (1990)Google Scholar
  7. 7.
    Linde, Y., Buzo, A., Gray, R.M.: An Algorithm for Vector Quantizer Design. IEEE Transaction on Communications 28, 84–95 (1980)CrossRefGoogle Scholar
  8. 8.
    Higgins, J.E., Damper, R.I., Harris, C.J.: A Multi-Spectral Data Fusion Approach to Speaker Recognition. In: Fusion 1999, 2nd International Conference on Information Fusion, Sunnyvale, CA, pp. 1136–1143 (1999)Google Scholar
  9. 9.
    Premakanthan, P., Mikhael, W.B.: Speaker Verification /Recognition and the Importance of Selective Feature Extraction:Review. In: Proc. of 44th IEEE MWSCAS 2001, vol. 1, pp. 57–61 (2001)Google Scholar
  10. 10.
    Razak, Z., Ibrahim, N.J., Idna Idris, M.Y., et al.: Quranic Verse Recitation Recognition Module for Support in J-QAF Learning: A Review. International Journal of Computer Science and Network Security (IJCSNS) 8(8), 207–216 (2008)Google Scholar
  11. 11.
    Becchetti, C., Ricotti, L.P.: Speech Recognition Theory and C++ Implementation. John Wiley & Sons, Chichester (1999)Google Scholar
  12. 12.
    Kittler, J., Hatef, M., Duin, R.P.W., Mates, J.: On Combining Classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(3), 226–239 (1998)CrossRefGoogle Scholar
  13. 13.
    Kuncheva, L.I., Bezdek, J.C., Duin, R.P.W.: Decision Templates for Multiple Classifier Fusion: An Experimental Comparison. Pattern Recognition 34(2), 299–314 (2001)CrossRefzbMATHGoogle Scholar
  14. 14.
    Shakhnarovivh, G., Darrel, T.: On Probabilistic Combination of face and Gait Cues for Identification. In: Proc. 5th IEEE Int’l Conf. Automatic Face Gesture Recognition, pp. 169–174 (2002)Google Scholar
  15. 15.
    Ho, T.K., Hull, J.J., Srihari, S.N.: Decision Combination in Multiple Classifier Systems. IEEE Transactions on Pattern Analysis and Machine Intelligence 16(12), 66–75 (1994)Google Scholar
  16. 16.
    Tumer, K., Ghosh, J.: Linear and Order Statistics Combiners for Pattern Classification. In: Sharkey, A. (ed.) Combining Artificial Neural Networks, pp. 127–162. Springer, Heidelberg (1999)Google Scholar
  17. 17.
    Chen, K., Chi, H.: A Method of Combining Multiple Probabilistic Classifiers through Soft Competition on Different Feature Sets. Neuro Computing 20(1-3), 227–252 (1998)zbMATHGoogle Scholar
  18. 18.
    Kuncheva, L.I., Jain, L.C.: Designing Classifier Fusion systems by Genetic Algorithms. IEEE Tran. on Evolutionary Computation 4(4), 327–336 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Ali Zulfiqar
    • 1
  • Aslam Muhammad
    • 2
  • Ana Maria Martinez-Enriquez
    • 3
  • G. Escalada-Imaz
    • 4
  1. 1.Departament of CS & ITUniversity of GujratPakistan
  2. 2.Departament of CS & EU. E. T.LahorePakistan
  3. 3.Department of Computer ScienceCINVESTAV-IPNMexico
  4. 4.Artificial Intelligence Research InstituteCSICBarcelonaSpain

Personalised recommendations