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In human-to-human interaction, people sometimes are able to pick up and respond sensitively to the other's internal state as it shifts moment by moment over the course of an exchange. To find out whether such an ability is worthwhile for computer human interfaces, we built a semi-automated tutoring-type spoken dialog system. The system inferred information about the user's 'ephemeral emotions', such as confidence, confusion, pleasure, and dependency, from the prosody of his utterances and the context. It used this information to select the most appropriate acknowledgement form at each moment. In doing so the system was following some of the basic social conventions for real-time interaction. Users rated the system with this ability more highly than a version without.

Abstract:The future of human-computer interfaces may include systems which are humanlike in abilities and behavior. One particularly interesting aspect of human-to-human communication is the ability of some conversation partners to sensitively pick up on the nuances of the other's utterances, as they shift from moment to moment, and to use this information to subtly adjust responses to express interest, supportiveness, sympathy, and the like. This paper reports a model of this ability in the context of a spoken dialog system for a tutoring-like interaction. The system used information about the user's internal state -such as feelings of confidence, confusion, pleasure, and dependency -as inferred from the prosody of his utterances and the context, and used this information to select the most appropriate acknowledgement form at each moment. Although straightforward rating reveals no significant preference for a system with this ability, a clear preference was found when users rated the system after listening to a recording of their interaction with it. This suggests that human-like, real-time sensitivity can be of value in interfaces. The paper further discusses ways to discover and quantify such rules of social interaction, using corpus-based-analysis, developer intuitions, and feedback from naive judges; and further suggests that the technique of 'evaluation after re-listening' is useful for evaluating spoken dialog systems which operate at near-human levels of performance.

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In this study, we proposed and implemented "MultiPod", an online educational system for learning foreign words. This system is based on iPods and it uses word learning materials of very short movies. Each learning material consists of a 5-second moving image that corresponds to the word to be learned, its spelling, and its pronunciation. We conducted an evaluation experiment with ten subjects in which we compared the learning method based on our system against the traditional paper-and-pen method. By the t-test for the results, we proved that there is a significant difference between the long-term effectiveness of MultiPod and that of the paper-andpen method.