  Deep learning models trained on audio-visual data have been successfully used
to achieve state-of-the-art performance for emotion recognition. In particular,
models trained with multitask learning have shown additional performance
improvements. However, such multitask models entangle information between the
tasks, encoding the mutual dependencies present in label distributions in the
real world data used for training. This work explores the disentanglement of
multimodal signal representations for the primary task of emotion recognition
and a secondary person identification task. In particular, we developed a
multitask framework to extract low-dimensional embeddings that aim to capture
emotion specific information, while containing minimal information related to
person identity. We evaluate three different techniques for disentanglement and
report results of up to 13% disentanglement while maintaining emotion
recognition performance.
