A Cross-Corpus Speech Emotion Recognition Method Based on Supervised Contrastive Learning

Abstract

Research on Speech Emotion Recognition (SER) often faces challenges such as the lack of large-scale public datasets and limited generalization capability when dealing with data from different distributions. To solve this problem, this paper proposes a cross-corpus speech emotion recognition method based on supervised contrast learning. The method employs a two-stage fine-tuning process: first, the self-supervised speech representation model is fine-tuned using supervised contrastive learning on multiple speech emotion datasets; then, the classifier is fine-tuned on the target dataset. The experimental results show that the WavLM-based model achieved unweighted accuracy (UA) of 77.41% on the IEMOCAP dataset and 96.49% on the CASIA dataset, outperforming the state-of-the-art results on the two datasets.