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

• URL: http://arxiv.org/abs/2310.04703v1
• Date: Sat, 7 Oct 2023 06:41:29 GMT
• Title: Integrating Contrastive Learning into a Multitask Transformer Model for Effective Domain Adaptation
• Authors: Chung-Soo Ahn, Jagath C. Rajapakse and Rajib Rana
• Abstract summary: We propose a novel domain adaptation technique that embodies a multitask framework with SER as the primary task. We show that our proposed model achieves state-of-the-art performance in SER within cross-corpus scenarios.
• Score: 4.157415305926585
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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