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 demonstrate 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 coefficient (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.

翻译：我们向2022年ICML Expressive Vocalizations Competition 2022年国际CML ExVo Form-Shot轨道提交了非语言声学(NVs)的情绪识别系统,拟议方法使用自监督的学习模型从NVs中提取特征,并使用分类链来模拟情感之间的依赖性标签。实验结果显示,拟议方法与若干基线方法相比,可以大大改进这项任务的绩效。我们提出的方法在验证集中获得了0.725美元的平均相近系数,在测试集中获得了0.739美元,而最佳基线方法在验证集中只获得0.554美元。我们在https://github.com/Aria-K-Alethia/ExVo公布了我们的代码,以帮助其他人复制我们的实验结果。