Improved feature data for robust speaker identification using hybrid gaussian mixture models - sequential minimal optimization system

International Review on Computers and Software

Volumn 4, Issue 3, 2009, Pages 344-350

  Zribi Bojelbene, S ^a   Ben Ayed Mezghani, D ^a   Ellouze, N ^a  

^a UNIVERSITY OF TUNIS EL MANAR   (Tunisia)

Author keywords

[No Author keywords available]

Indexed keywords

FEATURE DATA; GAUSSIAN MIXTURE MODEL; MALE SPEAKERS; MEL-FREQUENCY CEPSTRAL COEFFICIENTS; ROBUST SPEAKER IDENTIFICATION; SECOND DERIVATIVES; SEQUENTIAL MINIMAL OPTIMIZATION; TEXT-INDEPENDENT SPEAKER IDENTIFICATION; TRAINING SUPPORT VECTOR MACHINES;

HYBRID SYSTEMS; SUPPORT VECTOR MACHINES;

SPEECH RECOGNITION;

EID: 78651572718     PISSN: 18286003     EISSN: 18286011     Source Type: Journal    
DOI: None     Document Type: Article

References (27)

1. Reynolds D., An overview of automatic speaker recognition technology, Proceedings of the International Conference on Acoustics, Speech, and Signal Processing, Orlando, Florida, USA (pp. 4072-4075, 2002).
2. Reynolds D., Speaker identification and verification using Gaussian mixture speaker models, Speech Communication, vol. 17, (pp. 9l-108, 1995).
3. Reynolds D. A., Rose R. C., Robust text-independent speaker identification using Gaussian mixture speaker models. Proceedings of the IEEE Transaction of Speech Audio Process, vol. 3, n. 1 (pp. 72-83, 1995).
4. V. Vapnik, Estimation of Dependences Based on Empirical Data, In Springer (Ed.), (New York: Springer-Verlag, 1982).
5. Schmidt M., Gish H., Speaker Identification via Support Vector Machine, Proceedings of the International Conference on Acoustic Speech and Signal Process (pp. 105-108, Atlanta, 1996).
6. X. Dong, W. Zhaohui, Speaker Recognition Using Continuous Density Support Vector Machines, Electronics Letters, vol. 37, n. 17, pp. 1099-1101, 2001.
7. Wan V., Renals S., SVMSVM: Support Vector Machine Speaker Verification Methodology, Proceedings of the International Conference on Acoustics Speech and Signal Processing (pp. 22l-224, 2003).
8. Reynolds D., Quatieri T. F., Dunn R., Speaker verification using adapted Gaussian mixture models, Proceedings of Digital Signal Processing, vol. 10, n. 1-3 (pp. 19-41, 2000).
9. T. S. Jaakkola, D. Haussler, Exploiting generative models in discriminative classifiers, In M.S. Kearns, S.A. Solla and D.A. Cohn (Ed.), Advances in Neural Information Processing, 11 (Cambridge, MA, MIT Press, 1998, 487-493).
10. Hong Q. Y., Kwong S., A discriminative training approach for text-independent speaker recognition, Proceedings of the IEEE Signal Processing, vol. 85, n.7 (pp. 1449-1463, 2005).
11. A. Dempster, N. Laird, D. Rubin, Maximum likelihood from incomplete data via the EM algorithm, J. Royal Stat. Soc., vol. 39, pp. 1-38, 1977.
12. R. Duda, P. Hart, D. Stork, Pattern Classification, (2nd Ed.), (New York, Wiley, 2001).
13. Moreno P., Ho P., A New SVM Approach to Speaker Identification and Verification Using Probabilistic Distance Kernels, Proceedings of the European Conference on Speech Communication and Technology, (pp. 2965-2968, 2003).
14. B. E. Boser, I. Guyon, V. Vapnik, A Training Algorithm for Optimal Margin Classifiers, Proceedings of the Fifth Annual Workshop in Computational Learning Theory, springer, ACM Press, pp. 144-152, 1992.
15. C. J. C. Burges, A Tutorial on Support Vector Machines for Pattern Recognition, Data Mining and Knowledge Discovery, vol. 2, pp. 121-167, 1998.
16. J. Platt, Fast Training of Support Vector Machines Using Sequential Minimal Optimization, Advances in Kernel Methods - Support Vector Learning, MIT Press, pp. 185-208, 1998.
17. Osuna E., Freund R., Girosi F., Improved Training Algorithm for Support Vector Machines, Proceedings of the IEEE International Workshop on Neural Networks for Signal Processing, (pp. 276285, 1997).
18. Zribi Boujelbene S., Ben Ayed Mezghani D., Ellouze N., Identification du Locuteur par Système Hybride GMM-SMO, Proceedings of the 5th IEEE International Conference on Sciences of Electronics Technologies of Information and Telecommunications, 2009.
19. R. Vergin, B. O'Shaughnessy, A. Farhat, Generalized Mel Frequency Cepstral Coefficients for Large-Vocabulary Speaker independent Continuous-Speech Recognition, IEEE ASSP magazine, vol. 7, n. 5, pp. 525-532, 1999.
20. W. Zunjing, C. Zhigang, Improved MFCC-Based Feature for Robust Speaker Identification, Tsinghua Science and Technology, Vol. 10, n 2, pp. 158-161, 2005.
21. M. Faundez-Zanuy, M. Chetouani, Nonlinear Predictive Models: Overview and Possibilities in Speaker Recognition, Progress in Nonlinear Speech Processing, Springer, pp. 170-189, 2005.
22. Nosratighods M., Ambikairajah E., Epps J., Speaker Verification Using A Novel Set of Dynamic Features, Proceedings of the 18th International Conference on Pattern Recognition, Vol 4 (pp. 266-269, 2006).
23. F. D. Cilliers, Tree-based Gaussian Mixture Models for Speaker Verification, Ph.D. Thesis, Department of Electrical and Electronic Engineering, University of Stellenbosch, South Africa, 2005.
24. Zribi Boujelbene S., Ben Ayed Mezghani D., Ellouze N., Support Vector Machines Approaches and its Application to Speaker Identification, Proceedings of the IEEE International Conference on Digital Eco-Systems and Technologies (2009).
25. Zribi Boujelbene S., Ben Ayed Mezghani, D., Ellouze N., Vowel Phoneme Classification Using SMO Algorithm for Training Support Vector Machines, Proceedings of the 3rd IEEE International Conference on Information & Communication Technologies (pp. 1-5, 2008).
26. Zribi Boujelbene S., Ben Ayed Mezghani D., Ellouze N., Mise en Oeuvre et Comparaison des Techniques de Data Mining pour la Classification des Phonèmes de Types Voyelles, Proceedings of the Workshop Ecol 'IA Apprentissage et fouille de données de la théorie à la pratique (2008).
27. Zribi Boujelbene S., Ben Ayed Mezghani D., Ellouze N., Vowel Phoneme Classification Using Support Vector Machines, Proceedings of the IEEE International Conference on Digital Ecosystems and Technologies (2008).