The LAST MINUTE corpus comprises records and transcripts of naturalistic problem solving dialogs between N = 130 subjects and a companion system simulated in a Wizard of Oz experiment. Our goal is to detect dialog situations where subjects might break up the dialog with the system which might happen when the subject is unsuccessful. We present a dialog act based representation of the dialog courses in the problem solving phase of the experiment and propose and evaluate measures for dialog success or failure derived from this representation. This dialog act representation refines our previous coarse measure as it enables the correct classification of many dialog sequences that were ambiguous before. The dialog act representation is useful for the identification of different subject groups and the exploration of interesting dialog courses in the corpus. We find young females to be most successful in the challenging last part of the problem solving phase and young subjects to have the initiative in the dialog more often than the elderly.

We report about design and characteristics of the LAST MINUTE corpus. The recordings in this data collection are taken from a WOZ experiment that allows to investigate how users interact with a companion system in a mundane situation with the need for planning, re-planning and strategy change. The resulting corpus is distinguished with respect to aspects of size (e.g. number of subjects, length of sessions, number of channels, total length of records) as well as quality (e.g. balancedness of cohort, well designed scenario, standard based transcripts, psychological questionnaires, accompanying in-depth interviews).

The LAST MINUTE corpus comprises multimodal recordings (e.g. video, audio, transcripts) from WOZ interactions in a mundane planning task (Rösner et al., 2011). It is one of the largest corpora with naturalistic data currently available. In this paper we report about first results from attempts to automatically and manually analyze the different modes with respect to emotions and affects exhibited by the subjects. We describe and discuss difficulties encountered due to the strong contrast between the naturalistic recordings and traditional databases with acted emotions.

This paper reports on the creation of the multimodal NIMITEK corpus of affected behavior in human-machine interaction and its role in the development of the NIMITEK prototype system. The NIMITEK prototype system is a spoken dialogue system for supporting users while they solve problems in a graphics system. The central feature of the system is adaptive dialogue management. The system dynamically defines a dialogue strategy according to the current state of the interaction (including also the emotional state of the user). Particular emphasis is devoted to the level of naturalness of interaction. We discuss that a higher level of naturalness can be achieved by combining a habitable natural language interface and an appropriate dialogue strategy. The role of the NIMITEK multimodal corpus in achieving these requirements is twofold: (1) in developing the model of attentional state on the level of user’s commands that facilitates processing of flexibly formulated commands, and (2) in defining the dialogue strategy that takes the emotional state of the user into account. Finally, we sketch the implemented prototype system and describe the incorporated dialogue management module. Whereas the prototype system itself is task-specific, the described underlying concepts are intended to be task-independent.