In this study, we propose a methodology for the Emotional Mimicry Intensity (EMI) Estimation task within the context of the 6th Workshop and Competition on Affective Behavior Analysis in-the-wild. Our approach leverages the Wav2Vec 2.0 framework, pre-trained on a comprehensive podcast dataset, to extract a broad range of audio features encompassing both linguistic and paralinguistic elements. We enhance feature representation through a fusion technique that integrates individual features with a global mean vector, introducing global contextual insights into our analysis. Additionally, we incorporate a pre-trained valence-arousal-dominance (VAD) module from the Wav2Vec 2.0 model. Our fusion employs a Long Short-Term Memory (LSTM) architecture for efficient temporal analysis of audio data. Utilizing only the provided audio data, our approach demonstrates significant improvements over the established baseline.

翻译：在本研究中，我们针对第六届野外情感行为分析研讨会与竞赛中的情感模仿强度（EMI）估计任务提出了一种方法。该方法利用在大型播客数据集上预训练的Wav2Vec 2.0框架，提取涵盖语言和副语言要素的广泛音频特征。通过融合技术将个体特征与全局均值向量相结合，我们将全局上下文信息引入分析，从而增强特征表示。此外，我们整合了来自Wav2Vec 2.0模型的预训练效价-唤醒度-支配度（VAD）模块。该融合方法采用长短期记忆（LSTM）架构对音频数据进行高效时序分析。仅利用所提供的音频数据，本方法较既定基线取得了显著性能提升。