Advertisement

Scatter Difference NAP for SVM Speaker Recognition

  • Brendan Baker
  • Robbie Vogt
  • Mitchell McLaren
  • Sridha Sridharan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5558)

Abstract

This paper presents Scatter Difference Nuisance Attribute Projection (SD-NAP) as an enhancement to NAP for SVM-based speaker verification. While standard NAP may inadvertently remove desirable speaker variability, SD-NAP explicitly de-emphasises this variability by incorporating a weighted version of the between-class scatter into the NAP optimisation criterion. Experimental evaluation of SD-NAP with a variety of SVM systems on the 2006 and 2008 NIST SRE corpora demonstrate that SD-NAP provides improved verification performance over standard NAP in most cases, particularly at the EER operating point.

Keywords

Speaker Recognition Speaker Verification Speaker Information Speaker Variability Session Variability 
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.

References

  1. 1.
    Campbell, W.: Generalized linear discriminant sequence kernels for speaker recognition. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 1, pp. 161–164 (2002)Google Scholar
  2. 2.
    Stolcke, A., Ferrer, L., Kajarekar, S.: Improvements in MLLR-transform-based speaker recognition. In: Odyssey: The Speaker and Language Recognition Workshop (2006)Google Scholar
  3. 3.
    Campbell, W., Campbell, J., Reynolds, D., Jones, D., Leek, T.: Phonetic speaker recognition with support vector machines. In: Advances in Neural Information Processing Systems, vol. 16 (2004)Google Scholar
  4. 4.
    Campbell, W., Sturim, D., Reynolds, D., Solomonoff, A.: SVM based speaker verification using a GMM supervector kernel and NAP variability compensation. In: IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 1, pp. I–97–I–100 (2006)Google Scholar
  5. 5.
    Solomonoff, A., Quillen, C., Campbell, W.: Channel compensation for SVM speaker recognition. In: Odyssey: The Speaker and Language Recognition Workshop, pp. 57–62 (2004)Google Scholar
  6. 6.
    Solomonoff, A., Campbell, W., Boardman, I.: Advances in channel compensation for SVM speaker recognition. In: IEEE International Conference on Acoustics, Speech and Signal Processing, vol. I, pp. 629–632 (2005)Google Scholar
  7. 7.
    Vogt, R., Kajarekar, S., Sridharan, S.: Discriminant NAP for SVM speaker recognition. In: Odyssey: The Speaker and Language Recognition Workshop (2008)Google Scholar
  8. 8.
    Fukunaga, K.: Introduction to Statistical Pattern Recognition. Academic Press, San Diego (1990)Google Scholar
  9. 9.
    Liu, Q., Tang, X., Lu, H., Ma, S.: Face recognition using kernel scatter-difference-based discriminant analysis. IEEE Transactions on Neural Networks 17(4), 1081–1085 (2006)Google Scholar
  10. 10.
    Reynolds, D., Quatieri, T., Dunn, R.: Speaker verification using adapted Gaussian mixture models. Digital Signal Processing 10(1/2/3), 19–41 (2000)Google Scholar
  11. 11.
    McLaren, M., Vogt, R., Baker, B., Sridharan, S.: A comparison of session variability compensation techniques for SVM-based speaker recognition. In: Interspeech 2007, pp. 790–793 (2007)Google Scholar
  12. 12.
    Auckenthaler, R., Carey, M., Lloyd-Thomas, H.: Score normalization for text-independent speaker verification systems. Digital Signal Processing 10(1/2/3), 42–54 (2000)Google Scholar
  13. 13.
    Cieri, C., Miller, D., Walker, K.: The Fisher Corpus: a Resource for the Next Generations of Speech-to-Text. In: International Conference on Language Resources and Evaluation, pp. 69–71 (2004)Google Scholar
  14. 14.
    Hatch, A., Peskin, B., Stolcke, A.: Improved phonetic speaker recognition using lattice decoding. In: IEEE International Conference on Acoustics, Speech and Signal Processing (2005)Google Scholar
  15. 15.
    Andrews, W., Kohler, M., Campbell, J., Godfrey, J., Hernández-Cordero, J.: Gender-dependent phonetic refraction for speaker recognition. In: IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 1, pp. 149–152 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Brendan Baker
    • 1
  • Robbie Vogt
    • 1
  • Mitchell McLaren
    • 1
  • Sridha Sridharan
    • 1
  1. 1.Speech and Audio Research LaboratoryQueensland University of TechnologyBrisbaneAustralia

Personalised recommendations