MuSe-Personalisation: Personalisation Sub-Challenge (MuSe 2023)

<strong>Description</strong>: Predicting valence and arousal of individuals in a stressed disposition, as induced by the Trier Social Stress Test (TSST) protocol. Available modalities: audio, video, text and physiological signals (respiratory rate, ECG, BPM). <strong>Labels</strong>: The valence labels are obtained by fusing the ratings of three human annotators. For arousal, in contrast, two human annotations have been fused with the TSST subject's electrodermal activity (EDA) signal in order to obtain a more objective arousal gold standard. Hence, this sub-challenge is <strong>not</strong> identical to the 2021 MuSe-Stress sub-challenge. <strong>Dataset</strong>: MuSe-Stress is based on the Ulm-TSST data set as introduced in the MuSe 2021 challenge. It contains 69 recordings of individuals during a Trier Social Stress Test (TSST). <strong>In order to facilitate personalisation, parts of the test data labels are provided</strong>. Overall, about 6 hours of recordings are provided. The data is split in a speaker-independent manner with the training data set comprising 41 individuals, development and test each comprising 14 individuals. This split is identical to the split used in the MuSe 2022 challenge. <strong>General</strong>: The 4th Multimodal Sentiment Analysis Challenge and Workshop (MuSe) 2023 adresses research questions that are of interest to affective computing, machine learning and multimodal signal processing communities and encourages a fusion of their disciplines. The goal of the MuSe workshop and challenge is to gain new insights into the merits of each of the core modalities and to serve as a stimulating environment for the development and evaluation of multimodal affect recognition approaches.**描述**：本任务旨在预测经特里尔社会压力测试（Trier Social Stress Test，TSST）范式诱发应激状态后的个体的情感效价（valence）与唤醒度（arousal）。可用模态涵盖音频、视频、文本以及生理信号（呼吸频率、ECG、BPM）。 **标签**：情感效价标签通过融合三名人类标注员的评分得到。与之相对，唤醒度标签则将两名人类标注结果与TSST受试者的皮肤电活动（electrodermal activity，EDA）信号进行融合，以获取更客观的唤醒度金标准。因此，本次子挑战赛与2021年MuSe-Stress子挑战赛并不相同。 **数据集**：MuSe-Stress基于MuSe 2021挑战赛中提出的Ulm-TSST数据集构建，共包含69段受试者在特里尔社会压力测试过程中的录制数据。 **为便于个性化建模，本次挑战赛提供部分测试集标签**。整体而言，本次数据集共提供约6小时的录制数据。数据采用说话人独立的划分方式：训练集包含41名受试者，开发集与测试集各包含14名受试者。该划分方式与MuSe 2022挑战赛所使用的划分完全一致。 **概述**：2023年第四届多模态情感分析挑战赛与研讨会（Multimodal Sentiment Analysis Challenge and Workshop，MuSe）聚焦情感计算、机器学习与多模态信号处理领域共同关注的研究问题，并鼓励跨学科融合。本次MuSe研讨会与挑战赛的目标在于，深入探究各核心模态的应用价值，并为多模态情感识别方法的开发与评估提供优质的交流与验证平台。