This paper presents the evaluation of the PIT Corpus of multi-party dialogues recorded in a Wizard-of-Oz environment. An evaluation has been performed with two different foci: First, a usability evaluation was used to take a look at the overall ratings of the system. A shortened version of the SASSI questionnaire, namely the SASSISV, and the well established AttrakDiff questionnaire assessing the hedonistic and pragmatic dimension of computer systems have been analysed. In a second evaluation, the user's gaze direction was analysed in order to assess the difference in the user's (gazing) behaviour if interacting with the computer versus the other dialogue partner. Recordings have been performed in different setups of the system, e.g. with and without avatar. Thus, the presented evaluation further focuses on the difference in the interaction caused by deploying an avatar. The quantitative analysis of the gazing behaviour has resulted in several encouraging significant differences. As a possible interpretation it could be argued that users are more attentive towards systems with an avatar - the difference a face makes.

The PIT corpus is a German multi-media corpus of multi-party dialogues recorded in a Wizard-of-Oz environment at the University of Ulm. The scenario involves two human dialogue partners interacting with a multi-modal dialogue system in the domain of restaurant selection. In this paper we present the characteristics of the data which was recorded in three sessions resulting in a total of 75 dialogues and about 14 hours of audio and video data. The corpus is available at http://www.uni-ulm.de/in/pit.

In this paper we present the setup of an extensive Wizard-of-Oz environment used for the data collection and the development of a dialogue system. The envisioned Perception and Interaction Assistant will act as an independent dialogue partner. Passively observing the dialogue between the two human users with respect to a limited domain, the system should take the initiative and get meaningfully involved in the communication process when required by the conversational situation. The data collection described here involves audio and video data. We aim at building a rich multi-media data corpus to be used as a basis for our research which includes, inter alia, speech and gaze direction recognition, dialogue modelling and proactivity of the system. We further aspire to obtain data with emotional content to perfom research on emotion recognition, psychopysiological and usability analysis.