Word set probability boosting for improved spontaneous dialog recognition

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 dialogs. The single-pass adaptive boosting (AB) algorithm enables the language model weights to be tuned using the word set error criteria. In the two-pass version of the algorithm, the basic idea is to predict a set of words based on some a priori information, and perform a rescoring pass wherein the probabilities of the words in the predicted word set are amplified or boosted in some manner. An adaptive gradient descent procedure for tuning the word boosting factor is formulated, which enables the boost factors to be incrementally adjusted to maximize the accuracy of the speech recognition system outputs on held-out training data using the word set error criteria. Two novel models which predict the required word sets are presented: (i) utterance triggers, which capture within-utterance long-distance word interdependencies, and (ii) dialog triggers, which capture local temporal dialog-oriented word relations. The proposed trigger and adaptive boosting (TAB) algorithm, and the single-pass adaptive boosting (AB) algorithm are experimentally tested on a subset of the TRAINS-93 spontaneous dialogs and the TRAINS-95 semispontaneous corpus, and the results summarized