Dialog strategies for handling miscommunication in task-related HRI

As communication quality in public spaces often is impaired by noisy environment, it is difficult for a robot to retrieve missing task-information from humans. In this paper, different dialog strategies are modeled and evaluated with respect to user experience and error handling capabilities in HRI in order to cope with erroneous speech recognition. Since correct recognition of spoken language is a bottleneck for real-world dialog systems, special emphasis is placed on the issue of adapting dialog strategies to the conditions under which the dialog is held to thereby provide for adaptability of the dialog strategy to variable speech recognition performance. Experimental evaluations are conducted in a fully automated indoor setting, and in a Wizard-of-Oz outdoor setting. Results indicate that a critical point exists, up to which the use of requests for handling miscommunication improves the user experience of a dialog strategy.