In the recent years, automated speech recognition has been the main drive behind the advent of spoken language interfaces, but at the same time a severe limiting factor in the development of these systems. We believe that increased robustness in the face of recognition errors can be achieved by making the systems aware of their own misunderstandings, and employing appropriate recovery techniques when breakdowns in interaction occur. In this paper we address the first problem: the development of an utterance-level confidence annotator for a spoken dialog system. After a brief introduction to the CMU Communicator spoken dialog system (which provided the target platform for the developed annotator), we cast the confidence annotation problem as a machine learning classification task, and focus on selecting relevant features and on empirically identifying the best classification techniques for this task. The results indicate that significant reductions in classification error rate can be obtained using several different classifiers. Furthermore, we propose a data driven approach to assessing the impact of the errors committed by the confidence annotator on dialog performance, with a view to optimally fine-tuning the annotator. Several models were constructed, and the resulting error costs were in accordance with our intuition. We found, surprisingly, that, at least for a mixed-initiative spoken dialog system as the CMU Communicator, these errors trade-off equally over a wide operating characteristic

30 pages

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