  Cross-corpus speech emotion recognition (SER) aims to transfer emotional
knowledge from a labeled source corpus to an unlabeled corpus. However, prior
methods require access to source data during adaptation, which is unattainable
in real-life scenarios due to data privacy protection concerns. This paper
tackles a more practical task, namely source-free cross-corpus SER, where a
pre-trained source model is adapted to the target domain without access to
source data. To address the problem, we propose a novel method called
emotion-aware contrastive adaptation network (ECAN). The core idea is to
capture local neighborhood information between samples while considering the
global class-level adaptation. Specifically, we propose a nearest neighbor
contrastive learning to promote local emotion consistency among features of
highly similar samples. Furthermore, relying solely on nearest neighborhoods
may lead to ambiguous boundaries between clusters. Thus, we incorporate
supervised contrastive learning to encourage greater separation between
clusters representing different emotions, thereby facilitating improved
class-level adaptation. Extensive experiments indicate that our proposed ECAN
significantly outperforms state-of-the-art methods under the source-free
cross-corpus SER setting on several speech emotion corpora.
