  In this research, we introduce a novel methodology for assessing Emotional
Mimicry Intensity (EMI) as part of the 6th Workshop and Competition on
Affective Behavior Analysis in-the-wild. Our methodology utilises the Wav2Vec
2.0 architecture, which has been pre-trained on an extensive podcast dataset,
to capture a wide array of audio features that include both linguistic and
paralinguistic components. We refine our feature extraction process by
employing a fusion technique that combines individual features with a global
mean vector, thereby embedding a broader contextual understanding into our
analysis. A key aspect of our approach is the multi-task fusion strategy that
not only leverages these features but also incorporates a pre-trained
Valence-Arousal-Dominance (VAD) model. This integration is designed to refine
emotion intensity prediction by concurrently processing multiple emotional
dimensions, thereby embedding a richer contextual understanding into our
framework. For the temporal analysis of audio data, our feature fusion process
utilises a Long Short-Term Memory (LSTM) network. This approach, which relies
solely on the provided audio data, shows marked advancements over the existing
baseline, offering a more comprehensive understanding of emotional mimicry in
naturalistic settings, achieving the second place in the EMI challenge.
