Score Information Decision Fusion Using Support Vector Machine for a Correlation Filter Based Speaker Authentication System

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.

[1]  Kuldip K. Paliwal,et al.  Noise compensation in a multi-modal verification system , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[2]  Juha Röning,et al.  Combining classifiers with different footstep feature sets and multiple samples for person identification , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[3]  Sun-Yuan Kung,et al.  Multi-sample data-dependent fusion of sorted score sequences for biometric verification , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[4]  Douglas A. Reynolds,et al.  An overview of automatic speaker recognition technology , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[5]  Jr. J.P. Campbell,et al.  Speaker recognition: a tutorial , 1997, Proc. IEEE.

[6]  P. Khosla,et al.  Face Verification using Correlation Filters , 2002 .

[7]  Salina Abdul Samad,et al.  Person Identification Using Lip Motion Sequence , 2007, KES.

[8]  A.E. Rosenberg,et al.  Automatic speaker verification: A review , 1976, Proceedings of the IEEE.

[9]  Salina Abdul Samad,et al.  Lower face verification centered on lips using correlation filters , 2007 .

[10]  B. V. K. Vijaya Kumar,et al.  Fingerprint Verification Using Correlation Filters , 2003, AVBPA.

[11]  Samy Bengio,et al.  A multi-sample multi-source model for biometric authentication , 2002, Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing.

[12]  Roberto Brunelli,et al.  Person identification using multiple cues , 1995, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Lakhmi C. Jain,et al.  Knowledge-Based Intelligent Information and Engineering Systems , 2004, Lecture Notes in Computer Science.

[14]  William M. Campbell,et al.  Support vector machines for speaker verification and identification , 2000, Neural Networks for Signal Processing X. Proceedings of the 2000 IEEE Signal Processing Society Workshop (Cat. No.00TH8501).

[15]  D.A. Ramli,et al.  A multi-sample single-source model using spectrographic features for biometric authentication , 2007, 2007 6th International Conference on Information, Communications & Signal Processing.

[16]  S. Gunn.  Support Vector Machines for Classification and Regression , 1998 .

[17]  Jiri Matas,et al.  On Combining Classifiers , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Andrew Beng Jin Teoh,et al.  Nearest Neighbourhood Classifiers in a Bimodal Biometric Verification System Fusion Decision Scheme , 2004, J. Res. Pract. Inf. Technol..