Unimodal Multi-Task Fusion for Emotional Mimicry Prediction

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.

Unimodal Multi-Task Fusion for Emotional Mimicry Prediciton

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.

An Efficient Multitask Learning Architecture for Affective Vocal Burst Analysis

Affective speech analysis is an ongoing topic of research. A relatively new problem in this field is the analysis of vocal bursts, which are nonverbal vocalisations such as laughs or sighs. Current state-of-the-art approaches to address affective vocal burst analysis are mostly based on wav2vec2 or HuBERT features. In this paper, we investigate the use of the wav2vec successor data2vec in combination with a multitask learning pipeline to tackle different analysis problems at once. To assess the performance of our efficient multitask learning architecture, we participate in the 2022 ACII Affective Vocal Burst Challenge, showing that our approach substantially outperforms the baseline established there in three different subtasks.