Abstract
In this paper, we propose a novel decision fusion by fusing score information from multiple correlation filter outputs of a speaker authentication system. Correlation filter classifier is designed to yield a sharp peak in the correlation output for an authentic person while no peak is perceived for the imposter. By appending the scores from multiple correlation filter outputs as a feature vector, Support Vector Machine (SVM) is then executed for the decision process. In this study, cepstrumgraphic and spectrographic images are implemented as features to the system and Unconstrained Minimum Average Correlation Energy (UMACE) filters are used as classifiers. The first objective of this study is to develop a multiple score decision fusion system using SVM for speaker authentication. Secondly, the performance of the proposed system using both features are then evaluated and compared. The Digit Database is used for performance evaluation and an improvement is observed after implementing multiple score decision fusion which demonstrates the advantages of the scheme.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Campbell, J.P.: Speaker Recognition: A Tutorial. Proceeding of the IEEE 85, 1437–1462 (1997)
Rosenberg, A.: Automatic speaker verification: A review. Proceeding of IEEE 64(4), 475–487 (1976)
Reynolds, D.A.: An overview of Automatic Speaker Recognition Technology. Proceeding of IEEE on Acoustics Speech and Signal Processing 4, 4065–4072 (2002)
Poh, N., Bengio, S., Korczak, J.: A multi-sample multi-source model for biometric authentication. In: 10th IEEE on Neural Networks for Signal Processing, pp. 375–384 (2002)
Teoh, A., Samad, S.A., Hussein, A.: Nearest Neighborhood Classifiers in a Bimodal Biometric Verification System Fusion Decision Scheme. Journal of Research and Practice in Information Technology 36(1), 47–62 (2004)
Brunelli, R., Falavigna, D.: Personal Identification using Multiple Cue. Proceeding of IEEE Trans. on Pattern Analysis and Machine Intelligence 17(10), 955–966 (1995)
Suutala, J., Roning, J.: Combining Classifier with Different Footstep Feature Sets and Multiple Samples for Person Identification. In: Proceeeding of International Conference on Acoustics, Speech and Signal Processing, pp. 357–360 (2005)
Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On combining classifiers. Proceeding of IEEE Trans on Pattern Analysis and Machine Intelligence 20(3), 226–239 (1998)
Cheung, M.C., Mak, M.W., Kung, S.Y.: Multi-Sample Data-Dependent Fusion of Sorted Score Sequences for Biometric verification. In: IEEE Conference on Acoustics Speech and Signal Processing (ICASSP 2004), pp. 229–232 (2004)
Savvides, M., Vijaya Kumar, B.V.K., Khosla, P.: Face Verification using Correlation Filters. In: 3rd IEEE Automatic Identification Advanced Technologies, pp. 56–61 (2002)
Venkataramani, K., Vijaya Kumar, B.V.K.: Fingerprint Verification using Correlation Filters. In: System AVBPA, pp. 886–894 (2003)
Samad, S.A., Ramli, D.A., Hussain, A.: Lower Face Verification Centered on Lips using Correlation Filters. Information Technology Journal 6(8), 1146–1151 (2007)
Samad, S.A., Ramli, D.A., Hussain, A.: Person Identification using Lip Motion Sequence. In: Apolloni, B., Howlett, R.J., Jain, L. (eds.) KES 2007, Part I. LNCS (LNAI), vol. 4692, pp. 839–846. Springer, Heidelberg (2007)
Samad, S.A., Ramli, D.A., Hussain, A.: A Multi-Sample Single-Source Model using Spectrographic Features for Biometric Authentication. In: IEEE International Conference on Information, Communications and Signal Processing, CD ROM (2007)
Sanderson, C., Paliwal, K.K.: Noise Compensation in a Multi-Modal Verification System. In: Proceeding of International Conference on Acoustics, Speech and Signal Processing, pp. 157–160 (2001)
Spectrogram, http://cslu.cse.ogi.edu/tutordemo/spectrogramReading/spectrogram.html
Klevents, R.L., Rodman, R.D.: Voice Recognition: Background of Voice Recognition, London (1997)
Gunn, S.R.: Support Vector Machine for Classification and Regression. Technical Report, University of Southampton (2005)
Wan, V., Campbell, W.M.: Support Vector Machines for Speaker Verification and Identification. In: Proceeding of Neural Networks for Signal Processing, pp. 775–784 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ramli, D.A., Samad, S.A., Hussain, A. (2009). Score Information Decision Fusion Using Support Vector Machine for a Correlation Filter Based Speaker Authentication System. In: Corchado, E., Zunino, R., Gastaldo, P., Herrero, Á. (eds) Proceedings of the International Workshop on Computational Intelligence in Security for Information Systems CISIS’08. Advances in Soft Computing, vol 53. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88181-0_30
Download citation
DOI: https://doi.org/10.1007/978-3-540-88181-0_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88180-3
Online ISBN: 978-3-540-88181-0
eBook Packages: EngineeringEngineering (R0)