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We propose a novel Dynamic Restrained Uncertainty Weighting Loss toexperimentally handle the problem of balancing the contributions of multipletasks on the ICML ExVo 2022 Challenge. The multitask aims to recognizeexpressed emotions and demographic traits from vocal bursts jointly. Ourstrategy combines the advantages of Uncertainty Weight and Dynamic WeightAverage, by extending weights with a restraint term to make the learningprocess more explainable. We use a lightweight multi-exit CNN architecture toimplement our proposed loss approach. The experimental H-Mean score (0.394)shows a substantial improvement over the baseline H-Mean score (0.335).

Dynamic Restrained Uncertainty Weighting Loss for Multitask Learning of Vocal Expression