RESUMO
Listening to one another is essential to human-human interaction. In fact, we humans spend a substantial part of our day listening to other people, in private as well as in work settings. Attentive listening serves the function to gather information for oneself, but at the same time, it also signals to the speaker that he/she is being heard. To deduce whether our interlocutor is listening to us, we are relying on reading his/her nonverbal cues, very much like how we also use non-verbal cues to signal our attention. Such signaling becomes more complex when we move from dyadic to multi-party interactions. Understanding how humans use nonverbal cues in a multi-party listening context not only increases our understanding of human-human communication but also aids the development of successful human-robot interactions. This paper aims to bring together previous analyses of listener behavior analyses in human-human multi-party interaction and provide novel insights into gaze patterns between the listeners in particular. We are investigating whether the gaze patterns and feedback behavior, as observed in the human-human dialogue, are also beneficial for the perception of a robot in multi-party human-robot interaction. To answer this question, we are implementing an attentive listening system that generates multi-modal listening behavior based on our human-human analysis. We are comparing our system to a baseline system that does not differentiate between different listener types in its behavior generation. We are evaluating it in terms of the participant's perception of the robot, his behavior as well as the perception of third-party observers.
RESUMO
Engagement is a concept of the utmost importance in human-computer interaction, not only for informing the design and implementation of interfaces, but also for enabling more sophisticated interfaces capable of adapting to users. While the notion of engagement is actively being studied in a diverse set of domains, the term has been used to refer to a number of related, but different concepts. In fact it has been referred to across different disciplines under different names and with different connotations in mind. Therefore, it can be quite difficult to understand what the meaning of engagement is and how one study relates to another one accordingly. Engagement has been studied not only in human-human, but also in human-agent interactions i.e., interactions with physical robots and embodied virtual agents. In this overview article we focus on different factors involved in engagement studies, distinguishing especially between those studies that address task and social engagement, involve children and adults, are conducted in a lab or aimed for long term interaction. We also present models for detecting engagement and for generating multimodal behaviors to show engagement.
