论文标题
探索自我监管的学习和分类器链在情绪上识别非语言发声的有效性
Exploring the Effectiveness of Self-supervised Learning and Classifier Chains in Emotion Recognition of Nonverbal Vocalizations
论文作者
论文摘要
我们提出了一种针对非语言发声（NVS）的情感识别系统，该系统提交给ICML表达性发声竞赛的Exvo几乎没有曲目2022。拟议的方法使用自我监督的学习（SSL）模型来从NVS中提取功能，并使用分类器链来模拟情绪之间的标签依赖性。实验结果表明，与几种基线方法相比，所提出的方法可以显着改善该任务的性能。我们提出的方法在验证集中获得了平均一致性相关系数（CCC）为$ 0.725 $，在测试集中获得$ 0.739 $，而最佳基线方法在验证集中仅获得$ 0.554 $。我们在https://github.com/aria-k-alethia/exvo上发布代码，以帮助其他人复制我们的实验结果。
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.
