  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.
