Improved spontaneous dialogue recognition using dialogue and utterance triggers by adaptive probability boosting

Based on the observation that the unpredictable nature of conversational speech makes it almost impossible to reliably model sequential word constraints, the notion of word set error criteria is proposed for improved recognition of spontaneous dialogues. The basic idea in the TAB algorithm is to predict a set of words based on some a priori information, and perform a re-scoring pass wherein the probabilities of the words in the predicted word set are ampli ed or boosted in some manner. An adaptive gradient descent procedure for tuning the word boosting factor has been formulated. Two novel models which predict the required word sets have been presented: utterance triggers which capture within-utterance long-distance word inter-dependencies, and dialogue triggers which capture local temporal dialogue-oriented word relations. The proposed Trigger and Adaptive Boosting (TAB) algorithm have been experimentally tested on a subset of the TRAINS93 spontaneous dialogues and the TRAINS-95 semispontaneous corpus, and have resulted in improved performances.