Exploring the Effectiveness of Self-supervised Learning and Classifier Chains in Emotion Recognition of Nonverbal Vocalizations

We present an emotion recognition system for nonverbal vocalizations (NVs) submitted to the ExVo Few-Shot track of the ICML Expressive Vocalizations Competition 2022. The proposed method uses self-supervised learning (SSL) models to extract features from NVs and uses a classifier chain to model the label dependency between emotions. Experimental results demon-strate that the proposed method can significantly improve the performance of this task compared to several baseline methods. Our proposed method obtained a mean concordance correlation coeffi-cient (CCC) of 0 . 725 in the validation set and 0 . 739 in the test set, while the best baseline method only obtained 0 . 554 in the validation set. We publicate our code at https://github. com/Aria-K-Alethia/ExVo to help others to reproduce our experimental results.