Empirically evaluating an adaptable spoken dialogue system
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The paper address user behavior modeling in a machinemediated setting involving bidirectional speech translation. Specifically, usability data from doctor-patient dialogs involving a two way English-Persian speech translation system are analyzed to understand the nature, and extent, of user accommodation to machine errors. We consider user type "categorized along the classes of Accommodating, Normal and Picky " as it relates to the user's tendency to accept poor speech recognition and translation or retry to speak these again. For modeling, we employ a dynamic Bayesian network that can identify the user type with high accuracy after a few interactions of consistent user behavioral patterns. This model can be utilized for the design of machine strategies that can aid a user in operating the device more efficiently.