Unimodal Multi-Task Fusion for Emotional Mimicry Intensity Prediction

• URL: http://arxiv.org/abs/2403.11879v4
• Date: Sun, 16 Jun 2024 12:21:39 GMT
• Title: Unimodal Multi-Task Fusion for Emotional Mimicry Intensity Prediction
• Authors: Tobias Hallmen, Fabian Deuser, Norbert Oswald, Elisabeth André,
• Abstract summary: 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 utilise the Wav2Vec 2.0 architecture, which has been pre-trained on an extensive podcast dataset. We refine our feature extraction process by employing a fusion technique that combines individual features with a global mean vector.
• Score: 6.1058750788332325
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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