In this paper, we present our solution to the MuSe-Personalisation sub-challenge in the MuSe 2023 Multimodal Sentiment Analysis Challenge. The task of MuSe-Personalisation aims to predict the continuous arousal and valence values of a participant based on their audio-visual, language, and physiological signal modalities data. Considering different people have personal characteristics, the main challenge of this task is how to build robustness feature presentation for sentiment prediction. To address this issue, we propose exploiting diverse features. Specifically, we proposed a series of feature extraction methods to build a robust representation and model ensemble. We empirically evaluate the performance of the utilized method on the officially provided dataset. \textbf{As a result, we achieved 3rd place in the MuSe-Personalisation sub-challenge.} Specifically, we achieve the results of 0.8492 and 0.8439 for MuSe-Personalisation in terms of arousal and valence CCC.

翻译：本文介绍了我们在MuSe 2023多模态情感分析挑战赛中针对MuSe-Personalisation子挑战的解决方案。MuSe-Personalisation任务旨在基于参与者的视听、语言及生理信号模态数据，预测其连续的唤醒度和效价值。考虑到不同人群具有个性化特征，该任务的主要挑战是如何为情感预测构建稳健的特征表征。为解决这一问题，我们提出利用多样化特征。具体而言，我们设计了一系列特征提取方法以构建鲁棒性表征，并采用模型集成策略。我们基于官方提供的数据集对所采用方法进行了实证评估。\textbf{最终，我们在MuSe-Personalisation子挑战中取得第三名的成绩。}具体而言，在MuSe-Personalisation任务上，我们的唤醒度和效价CCC指标分别达到0.8492和0.8439。