论文标题
动态限制的不确定性加权损失，用于多任务的人声表达
Dynamic Restrained Uncertainty Weighting Loss for Multitask Learning of Vocal Expression
论文作者
论文摘要
我们提出了一种新型的动态限制性不确定性加权损失，以实验处理平衡多个任务对ICML EXVO 2022挑战的贡献的问题。该多任务旨在共同认识到人声爆发中表达的情绪和人口特征。我们的策略结合了不确定性重量和平均动态重量的优势，通过用约束术语扩展权重以使学习过程更具解释。我们使用轻巧的多exit CNN体系结构来实施我们提出的损失方法。实验性H-均值得分（0.394）比基线H-均值得分有了显着改善（0.335）。
We propose a novel Dynamic Restrained Uncertainty Weighting Loss to experimentally handle the problem of balancing the contributions of multiple tasks on the ICML ExVo 2022 Challenge. The multitask aims to recognize expressed emotions and demographic traits from vocal bursts jointly. Our strategy combines the advantages of Uncertainty Weight and Dynamic Weight Average, by extending weights with a restraint term to make the learning process more explainable. We use a lightweight multi-exit CNN architecture to implement our proposed loss approach. The experimental H-Mean score (0.394) shows a substantial improvement over the baseline H-Mean score (0.335).
