  Automatic speech emotion recognition provides computers with critical context
to enable user understanding. While methods trained and tested within the same
dataset have been shown successful, they often fail when applied to unseen
datasets. To address this, recent work has focused on adversarial methods to
find more generalized representations of emotional speech. However, many of
these methods have issues converging, and only involve datasets collected in
laboratory conditions. In this paper, we introduce Adversarial Discriminative
Domain Generalization (ADDoG), which follows an easier to train "meet in the
middle" approach. The model iteratively moves representations learned for each
dataset closer to one another, improving cross-dataset generalization. We also
introduce Multiclass ADDoG, or MADDoG, which is able to extend the proposed
method to more than two datasets, simultaneously. Our results show consistent
convergence for the introduced methods, with significantly improved results
when not using labels from the target dataset. We also show how, in most cases,
ADDoG and MADDoG can be used to improve upon baseline state-of-the-art methods
when target dataset labels are added and in-the-wild data are considered. Even
though our experiments focus on cross-corpus speech emotion, these methods
could be used to remove unwanted factors of variation in other settings.

Machine Learning;Sound;Audio and Speech Processing;Machine Learning