Integrating Contrastive Learning into a Multitask Transformer Model for
  Effective Domain Adaptation
Chung-Soo Ahn Jagath C. Rajapakse Rajib Rana
While speech emotion recognition (SER) research has made significant
progress, achieving generalization across various corpora continues to pose a
problem. We propose a novel domain adaptation technique that embodies a
multitask framework with SER as the primary task, and contrastive learning and
information maximisation loss as auxiliary tasks, underpinned by fine-tuning of
transformers pre-trained on large language models. Empirical results obtained
through experiments on well-established datasets like IEMOCAP and MSP-IMPROV,
illustrate that our proposed model achieves state-of-the-art performance in SER
within cross-corpus scenarios.