This paper describes how a speaker verification task can be advantageously decomposed into a series of binary classification problems, i.e. each problem discriminating between two classes only. Each binary classifier is specific to one speaker, one anti-speaker and one word. Decision trees dealing classifiers. The set of classifiers is then pruned to eliminate the less relevant ones. Diverse pruning methods are experimented, and it is shown that when the speaker verification decision is performed with an a priori threshold, some of them give better results than a reference HMM system.