Accommodating Missing Modalities in Time-Continuous Multimodal Emotion
  Recognition
Juan Vazquez-Rodriguez Grégoire Lefebvre Julien Cumin James L. Crowley
Decades of research indicate that emotion recognition is more effective when
drawing information from multiple modalities. But what if some modalities are
sometimes missing? To address this problem, we propose a novel
Transformer-based architecture for recognizing valence and arousal in a
time-continuous manner even with missing input modalities. We use a coupling of
cross-attention and self-attention mechanisms to emphasize relationships
between modalities during time and enhance the learning process on weak salient
inputs. Experimental results on the Ulm-TSST dataset show that our model
exhibits an improvement of the concordance correlation coefficient evaluation
of 37% when predicting arousal values and 30% when predicting valence values,
compared to a late-fusion baseline approach.