Directed decision trees for generating complementary systems

Many large vocabulary continuous speech recognition systems use a combination of multiple systems to obtain the final hypothesis. These complementary systems are typically found in an ad-hoc manner, by testing combinations of diverse systems and selecting the best. This paper presents a new algorithm for generating complementary systems by altering the decision tree generation, and a divergence measure for comparing decision trees. In this paper, the decision tree is biased against clustering states which have previously led to confusions. This leads to a system which concentrates states in contexts that were previously confusable. Thus these systems tend to make different errors. Results are presented on two broadcast news tasks – Mandarin and Arabic. The results show that combining multiple systems built from directed decision trees give gains in performance when confusion network combination is used as the method of combination. The results also show that the gains achieved using the directed tree algorithm are additive to the gains achieved using other techniques that have been empirically shown as complementary.