Spoken dialogue systems (SDSs) can be used to operate devices, e.g. in the automotive environment. People using these systems usually have different levels of experience. However, most systems do not take this into account. In this paper, we present a method to build a dialogue system in an automotive environment that automatically adapts to the user's experience with the system. We implemented the adaptation in a prototype and carried out exhaustive tests. Our usability tests show that adaptation increases both user performance and user satisfaction. We describe the tests that were performed, and the methods used to assess the test results. One of these methods is a modification of PARADISE, a framework for evaluating the performance of SDSs [Walker MA, Litman DJ, Kamm CA, Abella A (Comput Speech Lang 12(3):317-347, 1998)]. We discuss its drawbacks for the evaluation of SDSs like ours, the modifications we have carried out, and the test results.
We present an application independent dialogue engine that reasons on application dependent knowledge sources to calculate predictions about how a dialogue might continue. Predictions are language independent and are translated into language dependent structures for recognition and synthesis. Further, we discuss how the predictions account for different kinds of dialogue, e.g., question-answer or mixed initiative.
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