Comparison of different objective functions for optimal linear combination of classifiers for speaker identification

This paper presents a comparison of objective functions for optimally combining different speaker identification systems. The comparison is based on the classification performance of the resultant multiple classifier system (MCS). The objective functions considered are; classification figure of merit (CFM), mean square error (MSE) and cross entropy (CE). In all three methods, the outputs of individual classifiers assumed to be the posterior probabilities of each speaker and linear combination of the output vectors are considered. CFM seeks to maximize the difference between the output value of the speaker and the output values of all other incorrect speakers. On the other hand, MSE and CE compare the outputs with some ideal vectors where the output of the correct speaker is set to one and the others are zero. The experimental results are also compared with the averaging method where the combination is not optimized. Our simulation experiments on four different sets of speakers show that CFM performs better compared to the other objective functions