Stochastic language adaptation over time and state in natural spoken dialog systems

We are interested in adaptive spoken dialog systems for automated services. Peoples' spoken language usage varies over time for a given task, and furthermore varies depending on the state of the dialog. Thus, it is crucial to adapt automatic speech recognition (ASR) language models to these varying conditions. We characterize and quantify these variations based on a database of 30 K user-transactions with AT&T's experimental How May I Help You? spoken dialog system. We describe a novel adaptation algorithm for language models with time and dialog-state varying parameters. Our language adaptation framework allows for recognizing and understanding unconstrained speech at each stage of the dialog, 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 dialog states. We have achieved a reduction of 40% in perplexity and of 8.4% in word error rate over the baseline system, averaged across all dialog states.