Supervised Contrastive Learning with Nearest Neighbor Search for Speech Emotion Recognition

Speech Emotion Recognition (SER) is a challenging task due to limited data and blurred boundaries of certain emotions. In this paper, we present a comprehensive approach to improve the SER performance throughout the model lifecycle, including pre-training, fine-tuning, and inference stages. To address the data scarcity issue, we utilize a pre-trained model, wav2vec2.0. During fine-tuning, we propose a novel loss function that combines cross-entropy loss with supervised contrastive learning loss to improve the model's discriminative ability. This approach increases the inter-class distances and decreases the intra-class distances, mitigating the issue of blurred boundaries. Finally, to leverage the improved distances, we propose an interpolation method at the inference stage that combines the model prediction with the output from a k-nearest neighbors model. Our experiments on IEMOCAP demonstrate that our proposed methods outperform current state-of-the-art results.