A conversation analytic approach to the prediction of leadership in two to six-party audio conferences

Abstract

When evaluating the Quality of Experience (QoE) of audio conferences the participants talking behaviour is critical for their quality judgement. A dominant person, for instance, who talks and interacts a lot is likely to be more disturbed by conversation-related impairments than a less active person. Social interaction analysis provides reliable nonverbal audio features to predict dominance in face-to-face meetings. However, little is known about pure audio conferences. This is why this study investigates common dominance features for audio-only conferences. In the first part, we look at different group sizes and whether prediction models exist that predict the leader for two-to six-party audio-only conversations. In a second step, we apply the best model to an unknown audio conferencing dataset. Finally, we tested if dominance features are associated to the assigned caller or a particular person in each group and how the callers talking behaviour is related to perceived quality.