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Speech Emotion Recognition (SER) is a challenging task due to limited dataand blurred boundaries of certain emotions. In this paper, we present acomprehensive approach to improve the SER performance throughout the modellifecycle, including pre-training, fine-tuning, and inference stages. Toaddress the data scarcity issue, we utilize a pre-trained model, wav2vec2.0.During fine-tuning, we propose a novel loss function that combinescross-entropy loss with supervised contrastive learning loss to improve themodel's discriminative ability. This approach increases the inter-classdistances and decreases the intra-class distances, mitigating the issue ofblurred boundaries. Finally, to leverage the improved distances, we propose aninterpolation method at the inference stage that combines the model predictionwith the output from a k-nearest neighbors model. Our experiments on IEMOCAPdemonstrate that our proposed methods outperform current state-of-the-artresults.

Supervised Contrastive Learning with Nearest Neighbor Search for Speech Emotion Recognition