Advertisement

Particle Swarm Optimization for Feature Selection in Speaker Verification

  • Shahla Nemati
  • Mohammad Ehsan Basiri
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6024)

Abstract

The problem addressed in this paper concerns the feature subset selection for an automatic speaker verification system. An effective algorithm based on particle swarm optimization is proposed here for discovering the best feature combinations. After feature reduction phase, feature vectors are applied to a Gaussian mixture model which is a text-independent speaker verification model. The performance of proposed system is compared to the performance of a genetic algorithm-based system and the baseline algorithm. Experimentation is carried out, using TIMIT corpora. The results of experiments indicate that with the optimized feature subset, the performance of the system is improved. Moreover, the speed of verification is significantly increased since by use of PSO, number of features is reduced over 85% which consequently decrease the complexity of our ASV system.

Keywords

Particle Swarm optimization (PSO) Feature Selection (FS) Speaker Verification Gaussian Mixture Model (GMM) Genetic Algorithm (GA) 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ganchev, T., Zervas, P., Fakotakis, N., Kokkinakis, G.: Benchmarking Feature Selection Techniques on the Speaker Verification Task. In: Fifth International Symposium on Communication Systems, Networks And Digital Signal Processing, pp. 314–318 (2006)Google Scholar
  2. 2.
    Bimbot, F., et al.: A Tutorial on Text-Independent Speaker Verification. EURASIP Journal on Applied Signal Processing 4, 430–451 (2004)Google Scholar
  3. 3.
    Jensen, R.: Combining rough and fuzzy sets for feature selection. Ph.D. Thesis, University of Edinburgh (2005)Google Scholar
  4. 4.
    Nemati, S., Boostani, R., Jazi, M.D.: A Novel Text-Independent Speaker Verification System Using Ant Colony Optimization Algorithm. In: Elmoataz, A., Lezoray, O., Nouboud, F., Mammass, D. (eds.) ICISP 2008. LNCS, vol. 5099, pp. 421–429. Springer, Heidelberg (2008)Google Scholar
  5. 5.
    Nemati, S., Basiri, M.E., Ghasem-Aghaee, N., Aghdam, M.H.: A novel ACO-GA hybrid algorithm for feature selection in protein function prediction. Expert systems with applications 36, 12086–12094 (2009)CrossRefGoogle Scholar
  6. 6.
    Aghdam, M.H., Ghasem-Aghaee, N., Basiri, M.E.: Text Feature Selection using Ant Colony Optimization. Expert systems with applications 36, 6843–6853 (2009)CrossRefGoogle Scholar
  7. 7.
    Day, P., Nandi, A.K.: Robust Text-Independent Speaker Verification Using Genetic Programming. IEEE Transactions on Audio, Speech, and Language Processing 15, 285–295 (2007)CrossRefGoogle Scholar
  8. 8.
    Pandit, M., Kittkr, J.: Feature Selection for a DTW-Based Speaker Verification System, pp. 796–799. IEEE, Los Alamitos (1998)Google Scholar
  9. 9.
    Cohen, Zigel, Y.: On Feature Selection for Speaker Verification, of COST 275 workshop on The Advent of Biometrics on the Internet (2002)Google Scholar
  10. 10.
    Basiri, M.E., Ghasem-Aghaee, N., Aghdam, M.H.: Using ant colony optimization-based selected features for predicting post-synaptic activity in proteins. In: Marchiori, E., Moore, J.H. (eds.) EvoBIO 2008. LNCS, vol. 4973, pp. 12–23. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  11. 11.
    Liu, Y., Qin, Z., Xu, Z., He, X.: Feature Selection with Particle Swarms. In: Zhang, J., He, J.-H., Fu, Y. (eds.) CIS 2004. LNCS, vol. 3314, pp. 425–430. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  12. 12.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceeding of IEEE International Conference on Neural Networks, pp. 1942–1947. IEEE Press, Perth (1995)CrossRefGoogle Scholar
  13. 13.
    Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, pp. 39–43 (1995)Google Scholar
  14. 14.
    Poli, R., Kennedy, J., Blackwell, T.: Particle swarm optimization an overview. J. Swarm Intelligence 1, 33–57 (2007)CrossRefGoogle Scholar
  15. 15.
    Cheung-chi, L.: GMM-Based Speaker Recognition for Mobile Embedded Systems, Ph.D. Thesis, University of Hong Kong (2004)Google Scholar
  16. 16.
    Mladenič, D.: Feature Selection for Dimensionality Reduction. In: Saunders, C., Grobelnik, M., Gunn, S., Shawe-Taylor, J. (eds.) SLSFS 2005. LNCS, vol. 3940, pp. 84–102. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  17. 17.
    Bins, J.: Feature Selection from Huge Feature Sets in the Context of Computer Vision. Ph.D. dissertation, Department Computer Science, Colorado State University (2000)Google Scholar
  18. 18.
    Garofolo, J., et al.: DARPA TIMIT Acoustic-Phonetic Continuous Speech Corpus CD-ROM. National Institute of Standards and Technology (1990)Google Scholar
  19. 19.
    Trelea, I.C.: The particle swarm optimization algorithm: Convergence analysis and parameter selection. J. Inf. Process. Lett. 85, 317–325 (2003)zbMATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Shahla Nemati
    • 1
    • 2
  • Mohammad Ehsan Basiri
    • 1
    • 2
  1. 1.Arsanjan Branch, farsYoung Research Club, Islamic Azad UniversityIran
  2. 2.Department of Computer Engineering, Faculty of EngineeringUniversity of IsfahanIsfahanIran

Personalised recommendations