  This work analyzes the efficacy of verbal and nonverbal features of group
conversation for the task of automatic prediction of group task performance. We
describe a new publicly available survival task dataset that was collected and
annotated to facilitate this prediction task. In these experiments, the new
dataset is merged with an existing survival task dataset, allowing us to
compare feature sets on a much larger amount of data than has been used in
recent related work. This work is also distinct from related research on social
signal processing (SSP) in that we compare verbal and nonverbal features,
whereas SSP is almost exclusively concerned with nonverbal aspects of social
interaction. A key finding is that nonverbal features from the speech signal
are extremely effective for this task, even on their own. However, the most
effective individual features are verbal features, and we highlight the most
important ones.
