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We present an emotion recognition system for nonverbal vocalizations (NVs)submitted to the ExVo Few-Shot track of the ICML Expressive VocalizationsCompetition 2022. The proposed method uses self-supervised learning (SSL)models to extract features from NVs and uses a classifier chain to model thelabel dependency between emotions. Experimental results demonstrate that theproposed method can significantly improve the performance of this task comparedto several baseline methods. Our proposed method obtained a mean concordancecorrelation coefficient (CCC) of $0.725$ in the validation set and $0.739$ inthe test set, while the best baseline method only obtained $0.554$ in thevalidation set. We publicate our code at https://github.com/Aria-K-Alethia/ExVoto help others to reproduce our experimental results.

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