Complementary System Generation using Directed Decision Trees

Large vocabulary continuous speech recognition (LVCSR) systems often use a multi-pass decoding strategy with a combination of multiple systems in the final stage. To reduce the error rate, these models must be complementary, i.e. make different errors. Previously, complementary systems have been generated by independently training a number of models, explicitly performing all combinations and picking the best performance. This method becomes infeasible as the potential number of systems increases, and does not guarantee that any of the models will be complementary. This paper presents an algorithm for generating complementary systems by altering the decision tree generation. Confusions made by a baseline system are resolved by separating confusable states, which might previously have been clustered together using the standard decision tree algorithm. Experimental results presented on a broadcast news Mandarin task show gains when combining the baseline with a complementary directed decision tree system.