The MuSe 2023 is a set of shared tasks addressing three different contemporary multimodal affect and sentiment analysis problems: In the Mimicked Emotions Sub-Challenge (MuSe-Mimic), participants predict three continuous emotion targets. This sub-challenge utilises the Hume-Vidmimic dataset comprising of user-generated videos. For the Cross-Cultural Humour Detection Sub-Challenge (MuSe-Humour), an extension of the Passau Spontaneous Football Coach Humour (Passau-SFCH) dataset is provided. Participants predict the presence of spontaneous humour in a cross-cultural setting. The Personalisation Sub-Challenge (MuSe-Personalisation) is based on the Ulm-Trier Social Stress Test (Ulm-TSST) dataset, featuring recordings of subjects in a stressed situation. Here, arousal and valence signals are to be predicted, whereas parts of the test labels are made available in order to facilitate personalisation. MuSe 2023 seeks to bring together a broad audience from different research communities such as audio-visual emotion recognition, natural language processing, signal processing, and health informatics. In this baseline paper, we introduce the datasets, sub-challenges, and provided feature sets. As a competitive baseline system, a Gated Recurrent Unit (GRU)-Recurrent Neural Network (RNN) is employed. On the respective sub-challenges' test datasets, it achieves a mean (across three continuous intensity targets) Pearson's Correlation Coefficient of .4727 for MuSe-Mimic, an Area Under the Curve (AUC) value of .8310 for MuSe-Humor and Concordance Correlation Coefficient (CCC) values of .7482 for arousal and .7827 for valence in the MuSe-Personalisation sub-challenge.

翻译：MuSe 2023 是一组针对三个当代多模态情感和情感分析问题的共享任务：在模仿情绪子挑战（MuSe-Mimic）中，参与者需预测三个连续情感目标。该子挑战采用包含用户生成视频的Hume-Vidmimic数据集。跨文化幽默检测子挑战（MuSe-Humour）提供了帕绍自发性足球教练幽默数据集（Passau-SFCH）的扩展版本，参与者需在跨文化场景中预测自发性幽默的存在。个性化子挑战（MuSe-Personalisation）基于乌尔姆-特里尔社会压力测试数据集（Ulm-TSST），包含受试者在压力情境下的记录，需预测唤醒度和效价信号，同时提供部分测试标签以促进个性化。MuSe 2023 旨在汇聚来自视听情感识别、自然语言处理、信号处理和健康信息学等不同研究领域的广泛受众。本文作为基线论文，介绍了数据集、子挑战及提供的特征集。作为竞争性基线系统，我们采用了门控循环单元-循环神经网络（GRU-RNN）。在相应子挑战测试数据集上，该系统在MuSe-Mimic中取得了三个连续强度目标平均皮尔逊相关系数0.4727，在MuSe-Humor中取得了曲线下面积（AUC）值0.8310，在MuSe-Personalisation子挑战中取得了唤醒度一致性相关系数（CCC）0.7482和效价CCC值0.7827。