  Emotional expressiveness captures the extent to which a person tends to
outwardly display their emotions through behavior. Due to the close
relationship between emotional expressiveness and behavioral health, as well as
the crucial role that it plays in social interaction, the ability to
automatically predict emotional expressiveness stands to spur advances in
science, medicine, and industry. In this paper, we explore three related
research questions. First, how well can emotional expressiveness be predicted
from visual, linguistic, and multimodal behavioral signals? Second, which
behavioral modalities are uniquely important to the prediction of emotional
expressiveness? Third, which behavioral signals are reliably related to
emotional expressiveness? To answer these questions, we add highly reliable
transcripts and human ratings of perceived emotional expressiveness to an
existing video database and use this data to train, validate, and test
predictive models. Our best model shows promising predictive performance on
this dataset (RMSE=0.65, R^2=0.45, r=0.74). Multimodal models tend to perform
best overall, and models trained on the linguistic modality tend to outperform
models trained on the visual modality. Finally, examination of our
interpretable models' coefficients reveals a number of visual and linguistic
behavioral signals--such as facial action unit intensity, overall word count,
and use of words related to social processes--that reliably predict emotional
expressiveness.
