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。

情感模仿预测的单模态多任务融合

Unimodal Multi-Task Fusion for Emotional Mimicry Prediciton

In this paper, we present the solution to the Emotional Mimicry Intensity
(EMI) Estimation challenge, which is part of 6th Affective Behavior Analysis
in-the-wild (ABAW) Competition.The EMI Estimation challenge task aims to
evaluate the emotional intensity of seed videos by assessing them from a set of
predefined emotion categories (i.e., "Admiration," "Amusement,"
"Determination," "Empathic Pain," "Excitement," and "Joy").

本文介绍了解决情感模仿强度（EMI）估计挑战的方法，该挑战是第六届野外情感行为分析竞赛（ABAW）的一部分。EMI 估计挑战任务旨在通过从预定义的情绪类别（即 “崇拜”，“娱乐”，“决心”，“共情痛苦”，“兴奋” 和 “喜悦”）中评估它们来评估种子视频的情感强度。