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
this https URL

我们提出了一种新的 Emotion Decoupling aNd Alignment（EMO-DNA）学习框架，用于跨语料库语音情感识别（SER），通过对抗性解耦和双层情感对齐实现了学习情感相关的语料库不变特征，实验证明 EMO-DNA 在多个跨语料库场景中优于现有方法。