Integrating Contrastive Learning into a Multitask Transformer Model for Effective Domain Adaptation
Computation and Language
2023-10-10 v1 Human-Computer Interaction
Machine Learning
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.
Cite
@article{arxiv.2310.04703,
title = {Integrating Contrastive Learning into a Multitask Transformer Model for Effective Domain Adaptation},
author = {Chung-Soo Ahn and Jagath C. Rajapakse and Rajib Rana},
journal= {arXiv preprint arXiv:2310.04703},
year = {2023}
}