Toward Multimodal Modeling of Emotional Expressiveness

TL;DR

This study investigates how well emotional expressiveness can be predicted from visual, linguistic, and multimodal signals, revealing the importance of linguistic cues and specific behavioral signals for accurate prediction.

Contribution

It introduces a multimodal predictive framework for emotional expressiveness using enhanced datasets with human ratings and interpretable models.

Findings

Multimodal models outperform single-modality models.

Linguistic signals outperform visual signals in prediction.

Certain behavioral cues like facial action units and social words are key predictors.

Abstract

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.