  Listeners use short interjections, so-called backchannels, to signify
attention or express agreement. The automatic analysis of this behavior is of
key importance for human conversation analysis and interactive conversational
agents. Current state-of-the-art approaches for backchannel analysis from
visual behavior make use of two types of features: features based on body pose
and features based on facial behavior. At the same time, transformer neural
networks have been established as an effective means to fuse input from
different data sources, but they have not yet been applied to backchannel
analysis. In this work, we conduct a comprehensive evaluation of multi-modal
transformer architectures for automatic backchannel analysis based on pose and
facial information. We address both the detection of backchannels as well as
the task of estimating the agreement expressed in a backchannel. In evaluations
on the MultiMediate'22 backchannel detection challenge, we reach 66.4% accuracy
with a one-layer transformer architecture, outperforming the previous state of
the art. With a two-layer transformer architecture, we furthermore set a new
state of the art (0.0604 MSE) on the task of estimating the amount of agreement
expressed in a backchannel.
