In this paper, utterance verification based on hybrid scores obtained from three pairs of models is investigated. The three models considered are the on-line garbage model, the antiword function model and a model derived using Kullback-Leibler divergence. The performance of utterance verification algorithm using hypothesis testing depends on the accuracy of the estimate of the alternative hypothesis. The three models offer different perspectives in the probability estimation of the alternate hypothesis. Performance comparison between hybrid scores using different model pairs is made. In addition, performance improvement over conventional algorithm is experimentally verified.