Hybrid Mutimodal Fusion for Dimensional Emotion Recognition

TL;DR

This paper presents a multimodal fusion approach using LSTM with self-attention and late fusion strategies for continuous emotion and physiological arousal recognition, achieving top-ranked results in the MuSe 2021 challenges.

Contribution

It introduces a novel multimodal fusion framework combining LSTM, self-attention, and late fusion for emotion and physiological arousal recognition in stress scenarios.

Findings

Achieved top 3 ranking in MuSe-Stress and MuSe-Physio challenges.

Attained CCC of 0.6159 for valence and 0.4609 for arousal.

Achieved CCC of 0.5412 for physiological arousal.

Abstract

In this paper, we extensively present our solutions for the MuSe-Stress sub-challenge and the MuSe-Physio sub-challenge of Multimodal Sentiment Challenge (MuSe) 2021. The goal of MuSe-Stress sub-challenge is to predict the level of emotional arousal and valence in a time-continuous manner from audio-visual recordings and the goal of MuSe-Physio sub-challenge is to predict the level of psycho-physiological arousal from a) human annotations fused with b) galvanic skin response (also known as Electrodermal Activity (EDA)) signals from the stressed people. The Ulm-TSST dataset which is a novel subset of the audio-visual textual Ulm-Trier Social Stress dataset that features German speakers in a Trier Social Stress Test (TSST) induced stress situation is used in both sub-challenges. For the MuSe-Stress sub-challenge, we highlight our solutions in three aspects: 1) the audio-visual features and the bio-signal features are used for emotional state recognition. 2) the Long Short-Term Memory (LSTM) with the self-attention mechanism is utilized to capture complex temporal dependencies within the feature sequences. 3) the late fusion strategy is adopted to further boost the model's recognition performance by exploiting complementary information scattered across multimodal sequences. Our proposed model achieves CCC of 0.6159 and 0.4609 for valence and arousal respectively on the test set, which both rank in the top 3. For the MuSe-Physio sub-challenge, we first extract the audio-visual features and the bio-signal features from multiple modalities. Then, the LSTM module with the self-attention mechanism, and the Gated Convolutional Neural Networks (GCNN) as well as the LSTM network are utilized for modeling the complex temporal dependencies in the sequence. Finally, the late fusion strategy is used. Our proposed method also achieves CCC of 0.5412 on the test set, which ranks in the top 3.