3 research outputs found
Emergent Leadership Detection Across Datasets
Automatic detection of emergent leaders in small groups from nonverbal
behaviour is a growing research topic in social signal processing but existing
methods were evaluated on single datasets -- an unrealistic assumption for
real-world applications in which systems are required to also work in settings
unseen at training time. It therefore remains unclear whether current methods
for emergent leadership detection generalise to similar but new settings and to
which extent. To overcome this limitation, we are the first to study a
cross-dataset evaluation setting for the emergent leadership detection task. We
provide evaluations for within- and cross-dataset prediction using two current
datasets (PAVIS and MPIIGroupInteraction), as well as an investigation on the
robustness of commonly used feature channels (visual focus of attention, body
pose, facial action units, speaking activity) and online prediction in the
cross-dataset setting. Our evaluations show that using pose and eye contact
based features, cross-dataset prediction is possible with an accuracy of 0.68,
as such providing another important piece of the puzzle towards emergent
leadership detection in the real world.Comment: 5 pages, 3 figure
Detecting Low Rapport During Natural Interactions in Small Groups from Non-Verbal Behaviour
Rapport, the close and harmonious relationship in which interaction partners
are "in sync" with each other, was shown to result in smoother social
interactions, improved collaboration, and improved interpersonal outcomes. In
this work, we are first to investigate automatic prediction of low rapport
during natural interactions within small groups. This task is challenging given
that rapport only manifests in subtle non-verbal signals that are, in addition,
subject to influences of group dynamics as well as inter-personal
idiosyncrasies. We record videos of unscripted discussions of three to four
people using a multi-view camera system and microphones. We analyse a rich set
of non-verbal signals for rapport detection, namely facial expressions, hand
motion, gaze, speaker turns, and speech prosody. Using facial features, we can
detect low rapport with an average precision of 0.7 (chance level at 0.25),
while incorporating prior knowledge of participants' personalities can even
achieve early prediction without a drop in performance. We further provide a
detailed analysis of different feature sets and the amount of information
contained in different temporal segments of the interactions.Comment: 12 pages, 6 figure
Discriminating individually considerate and authoritarian leaders by speech activity cues
Effective leadership can increase team performance, however up to now the influence of specific micro-level behavioral
patterns on team performance is unclear. At the same time,
current behavior observation methods in social psychology mostly rely on manual video annotations that impede research. In our work, we follow a sensor-based approach to automatically extract speech activity cues to discriminate individualized considerate from authoritarian leadership. On a subset of 35 selected group discussions lead by leaders of different styles, we predict leadership style with 75.5% accuracy using logistic regression.
We find that leadership style predictability is dependent on the relative discussion time and is highest for the middle parts of the discussions. Analysis of regression coefficients suggests that individually considerate leaders start speaking more often while others speak, use short utterances more often, change their speech loudness more and speak less than authoritarian leaders