Building multiple complementary systems using directed decision trees

Large vocabulary speech recognition systems typically use a combination of multiple systems to obtain the final hypothesis. For combination to give gains, the systems being combined must be complementary, i.e. they must make different errors. Often, complementary systems are chosen simply by training multiple systems, performing all combinations, and selecting the best. This approach becomes time consuming as more potential systems are considered, and hence recent work has looked at explicitly building systems to be complementary to each other. This paper considers building multiple complementary systems based on directed decision trees, and combining them within a multi-pass adaptive framework. The tree divergence is introduced for easy comparison of trees without having to build entire systems. Experiments are presented on a Broadcast News Arabic task, and show that gains can be achieved by using more than one complementary system.