  Cross-corpus speech emotion recognition (SER) seeks to generalize the ability
of inferring speech emotion from a well-labeled corpus to an unlabeled one,
which is a rather challenging task due to the significant discrepancy between
two corpora. Existing methods, typically based on unsupervised domain
adaptation (UDA), struggle to learn corpus-invariant features by global
distribution alignment, but unfortunately, the resulting features are mixed
with corpus-specific features or not class-discriminative. To tackle these
challenges, we propose a novel Emotion Decoupling aNd Alignment learning
framework (EMO-DNA) for cross-corpus SER, a novel UDA method to learn
emotion-relevant corpus-invariant features. The novelties of EMO-DNA are
two-fold: contrastive emotion decoupling and dual-level emotion alignment. On
one hand, our contrastive emotion decoupling achieves decoupling learning via a
contrastive decoupling loss to strengthen the separability of emotion-relevant
features from corpus-specific ones. On the other hand, our dual-level emotion
alignment introduces an adaptive threshold pseudo-labeling to select confident
target samples for class-level alignment, and performs corpus-level alignment
to jointly guide model for learning class-discriminative corpus-invariant
features across corpora. Extensive experimental results demonstrate the
superior performance of EMO-DNA over the state-of-the-art methods in several
cross-corpus scenarios. Source code is available at
https://github.com/Jiaxin-Ye/Emo-DNA.
