Coupled Sparse Subspace Alignment-Based Domain Adaptation for Speech Emotion Recognition

Speech emotion recognition (SER) is crucial for human–computer interaction (HCI), yet remains a challenge in cross-domain scenarios. Emotional expressions vary significantly across speakers, languages, and recording conditions, leading to serious domain shifts. Coupled subspace learning has recently attracted considerable attention in domain adaptation (DA) as an effective approach to mitigating domain discrepancies by capturing common and domain-specific information. However, existing algorithms suffer from two limitations: 1) most methods directly adopt classifiers [e.g., support vector machine (SVM)] to incorporate discriminative information of the target domain, but such strategies lack flexibility and adaptability; and 2) the features learned from coupled projection matrices are redundant and poorly discriminative. To address these issues, we propose a novel DA approach named coupled sparse subspace alignment (CSSA) for cross-domain SER. Specifically, CSSA first performs latent representation learning on the unlabeled target domain data, in which the latent representation matrix is then optimized into a pseudolabel matrix to provide emotional guidance. Meanwhile, it models the source and target domains separately by sparse regression, thereby learning both discriminative and domain-specific information. Subsequently, CSSA performs coupled subspace alignment to reduce the domain discrepancy, where the dual projection matrices are progressively aligned to enhance their similarity. Additionally, a graph Laplacian regularization is applied to the cross-domain data to capture the local geometric structure. Extensive experiments on five public SER datasets demonstrate the superiority of CSSA over state-of-the-art DA methods.