Timing is Everything: Identifying Diverse Interaction Dynamics in Scenario and Non-Scenario Meetings

Abstract

In this paper we explore the use of temporal patterns to define interaction dynamics between different kinds of meetings. Meetings occur on a daily basis and include different behavioral dynamics between participants, such as floor shifts and intense dialog. These dynamics can tell a story of the meeting and provide insight into how participants interact. We focus our investigation on defining diversity metrics to compare the interaction dynamics of scenario and non-scenario meetings. These metrics may be able to provide insight into the similarities and differences between scenario and non-scenario meetings. We observe that certain interaction dynamics can be identified through temporal patterns of speech intervals, i.e., when a participant is talking. We apply the principles of Parallel Episodes in identifying moments of speech overlap, e.g., interaction "bursts", and introduce Situated Data Mining, an approach for identifying repeated behavior patterns based on situated context. Applying these algorithms provides an overview of certain meeting dynamics and defines metrics for meeting comparison and diversity of interaction. We tested on a subset of the AMI corpus and developed three diversity metrics to describe similarities and differences between meetings. These metrics also present the researcher with an overview of interaction dynamics and presents points-of-interest for analysis.