An information theoretic framework for weight estimation in the combination of probabilistic classifiers for speaker identification

Abstract

In this paper, we describe a relation between classification systems and information transmission systems. By looking at the classification systems from this perspective, we propose a method of classifier weight estimation for the linear (LIN-OP) and logarithmic opinion pool (LOG-OP) type classifier combination schemes for which some tools from information theory are used. These weights provide contextual information about the classifiers such as class dependent classifier reliability and global classifier reliability. A measure for decision consensus among the classifiers is also proposed which is formulated as a multiplicative part of the classifier weights. A method of selecting the classifiers which provide complementary information for the combination operation is given. Using the proposed method, two classifiers are selected to be used in the combination operation. Simulation experiments in closed set speaker identification have shown that the method of weight estimation described in this paper improved the identification rates of both linear and logarithmic opinion type combination schemes. A comparison between the proposed method and some other methods of weight selection is also given at the end of the paper.