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While speech emotion recognition (SER) research has made significantprogress, achieving generalization across various corpora continues to pose aproblem. We propose a novel domain adaptation technique that embodies amultitask framework with SER as the primary task, and contrastive learning andinformation maximisation loss as auxiliary tasks, underpinned by fine-tuning oftransformers pre-trained on large language models. Empirical results obtainedthrough experiments on well-established datasets like IEMOCAP and MSP-IMPROV,illustrate that our proposed model achieves state-of-the-art performance in SERwithin cross-corpus scenarios.

Integrating Contrastive Learning into a Multitask Transformer Model for Effective Domain Adaptation