Advertisement

Robust Text-Independent Speaker Identification Using Hybrid PCA&LDA

  • Min-Seok Kim
  • Ha-Jin Yu
  • Keun-Chang Kwak
  • Su-Young Chi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4293)

Abstract

We have been building a text-independent speaker recognition system in noisy conditions. In this paper, we propose a novel feature using hybrid PCA/LDA. The feature is created from the convectional MFCC(mel-frequency cepstral coefficients) by transforming them using a matrix. The matrix consists of some components from the PCA and LDA transformation matrices. We tested the new feature using Aurora project Database 2 which is intended for the evaluation of algorithms for front-end feature extraction algorithms in background noise. The proposed method outperformed in all noise types and noise levels. It reduced the relative recognition error by 63.6% than using the baseline feature when the SNR is 15dB.

Keywords

Face Recognition Linear Discriminant Analysis Gaussian Mixture Model Principal Component Analysis Automatic Speech Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Campbell, J.P.: Speaker Recognition: A Tutorial. Proceedings of the IEEE 85(9), 1437–1462 (1997)CrossRefGoogle Scholar
  2. 2.
    Acero, A.: Acoustical and Environmental Robustness in Automatic Speech Recognition. Kluwer Academic Publishers, Boston (1993)Google Scholar
  3. 3.
    Huang, X., Acero, A., Hon, H.: Spoken Language Processing, A Guide to Theory, Algorithm, and System Development. Prentice-Hall, Englewood Cliffs (2001)Google Scholar
  4. 4.
    Tsai, S.-N., Lee, L.-S.: Improved Robust Features for Speech Recognition by Integrating Time-Frequency Principal Components (TFPC) and Histogram Equalization (HEQ). In: IEEE Workshop on Automatic Speech Recognition and Understanding, pp. 297–302 (2003)Google Scholar
  5. 5.
    Wanfeng, Z., Yingchun, Y., Zhaohui, W., Lifeng, S.: Experimental Evaluation of a New Speaker Identification Framework using PCA. In: IEEE International Conference on Systems, Man and Cybernetics, vol. 5, pp. 4147–4152 (2003)Google Scholar
  6. 6.
    Ding, P., Liming, Z.: Speaker Recognition using Principal Component Analysis. In: Proceedings of ICONIP 2001, 8th International Conference on Neural Information Processing, Shanghai (2001)Google Scholar
  7. 7.
    Jin, Q., Waibel, A.: Application of LDA to Speaker Recognition. In: International Conference on Speech and Language Processing, October 2000, Beijing, China (2000)Google Scholar
  8. 8.
    Openshaw, J.P., Sun, Z.P., Mason, J.S.: A comparison of composite features under degraded speech in speaker recognition. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 1993, April 1993, vol. 2, pp. 371–374 (1993)Google Scholar
  9. 9.
    Su, H.-T., Feng, D.-D., Wang, X.-Y., Zhao, R.-C.: Face Recognition Using Hybrid Feature. In: Proceedings of the Second International Conference on Machine Learning and Cybernetics, Xi’an, November 2003, pp. 3045–3049 (2003)Google Scholar
  10. 10.
    Zhao, W., Chellappa, R., Krishnaswamy, A.: Discriminant analysis of principal components for face recognition. In: Proceedings of the Third IEEE International Conference on Automatic Face and Gesture Recognition, April 1998, pp. 336–341 (1998)Google Scholar
  11. 11.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley-Interscience, Chichester (2000)Google Scholar
  12. 12.
    Reynolds, D.A., Rose, R.C.: Robust Text-Independent Speaker Identification Using Gaussian Mixture Speaker Models. IEEE Transactions on Speech Audio Processing 3(1), 72–83 (1995)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Min-Seok Kim
    • 1
  • Ha-Jin Yu
    • 1
  • Keun-Chang Kwak
    • 2
  • Su-Young Chi
    • 2
  1. 1.School of Computer ScienceUniversity of SeoulSeoulKorea
  2. 2.Human-Robot Interaction Research Team, Intelligent Robot Research DivisionElectronics and Telecommunication Research Institute (ETRI)Korea

Personalised recommendations