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.

通过利用 Wav2Vec 2.0 框架进行语言和语音特征提取，以及 LSTM 架构对音频数据进行时序分析，本研究提出了一种在情感行为分析竞赛中对情感模仿强度进行估计的方法，取得了显著的改进。