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.