Stochastic Language Adaptation over Time andState in Natural Spoken Dialogue

| We are interested in adaptive spoken dialogue systems for automated services. Peoples' spoken language usage varies over time for a given task, and furthermore varies depending on the state of the dialogue. Thus, it is crucial to adapt ASR language models to these varying conditions. We characterize and quantify these variations based on a database of 30K user-transactions with AT&T's experimental How May I Help You ? spoken dialogue system. We describe a novel adaptation algorithm for language models with time and dialogue-state varying parameters. Our language adaptation framework allows for recognizing and understanding unconstrained speech at each stage of the dialogue , enabling context-switching and error recovery. These models have been used to train state-dependent ASR language models. We have evaluated their performance with respect to word accuracy and perplexity over time and dialogue states. We have achieved a reduction of 40% in per-plexity and of 8:4% in word error rate over the baseline system , averaged across all dialogue states.