Versatile Audio-Visual Learning for Handling Single and Multi Modalities
  in Emotion Regression and Classification Tasks
Lucas Goncalves Seong-Gyun Leem Wei-Cheng Lin Berrak Sisman Carlos Busso
Most current audio-visual emotion recognition models lack the flexibility
needed for deployment in practical applications. We envision a multimodal
system that works even when only one modality is available and can be
implemented interchangeably for either predicting emotional attributes or
recognizing categorical emotions. Achieving such flexibility in a multimodal
emotion recognition system is difficult due to the inherent challenges in
accurately interpreting and integrating varied data sources. It is also a
challenge to robustly handle missing or partial information while allowing
direct switch between regression and classification tasks. This study proposes
a \emph{versatile audio-visual learning} (VAVL) framework for handling unimodal
and multimodal systems for emotion regression and emotion classification tasks.
We implement an audio-visual framework that can be trained even when audio and
visual paired data is not available for part of the training set (i.e., audio
only or only video is present). We achieve this effective representation
learning with audio-visual shared layers, residual connections over shared
layers, and a unimodal reconstruction task. Our experimental results reveal
that our architecture significantly outperforms strong baselines on both the
CREMA-D and MSP-IMPROV corpora. Notably, VAVL attains a new state-of-the-art
performance in the emotional attribute prediction task on the MSP-IMPROV
corpus. Code available at: https://github.com/ilucasgoncalves/VAVL