Automatic perception and understanding of human emotion or
sentiment has a wide range of applications and has attracted increasing attention nowadays. The Multimodal Sentiment Analysis
in Real-life Media (MuSe) 2020 provides a testing bed for recognizing human emotion or sentiment from multiple modalities (audio,
video, and text) in the wild scenario. In this paper, we present our
solutions to the MuSe-Wild sub-challenge of MuSe 2020. The goal
of this sub-challenge is to perform continuous emotion (arousal
and valence) predictions on a car review database, Muse-CaR. To
this end, we frst extract both handcrafted features and deep representations from multiple modalities. Then, we utilize the Long
Short-Term Memory (LSTM) recurrent neural network as well as
the self-attention mechanism to model the complex temporal dependencies in the sequence. The Concordance Correlation Coefcient
(CCC) loss is employed to guide the model to learn local variations
and the global trend of emotion simultaneously. Finally, two fusion
strategies, early fusion and late fusion, are adopted to further boost
the model’s performance by exploiting complementary information
from different modalities. Our proposed method achieves CCC of
0.4726 and 0.5996 for arousal and valence respectively on the test
set, which outperforms the baseline system with corresponding
CCC of 0.2834 and 0.2431.