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In a spoken dialogue system, the degree to which the dialogue manager informs and controls the behavior of other human language technology components is an important research topic. Although each separate server can be developed and trained on its own, it must function as part of an entire system, and do so in the context of a complex dialogue with a human user. The dialogue manager is the one component that has not only local information from each server, but also global knowledge about the task and specific knowledge about a particular user's constraints. In this paper, we describe various algorithms we have developed for exploiting the knowledge of the dialogue manager in the selection of recognition hypotheses in the context of human-machine interactions. We describe enhancements we have made to other human language technology servers for the purpose of providing useful information to the dialogue manager, as well as new capabilities in the dialogue manager itself aimed at detecting and repairing problematic spots in the dialogue. We conclude by describing some evaluation metrics and tools we have developed for monitoring system performance.

Hypothesis selection and resolution in the mercury flight reservation system