  The continuous dimensional emotion modelled by arousal and valence can depict
complex changes of emotions. In this paper, we present our works on arousal and
valence predictions for One-Minute-Gradual (OMG) Emotion Challenge. Multimodal
representations are first extracted from videos using a variety of acoustic,
video and textual models and support vector machine (SVM) is then used for
fusion of multimodal signals to make final predictions. Our solution achieves
Concordant Correlation Coefficient (CCC) scores of 0.397 and 0.520 on arousal
and valence respectively for the validation dataset, which outperforms the
baseline systems with the best CCC scores of 0.15 and 0.23 on arousal and
valence by a large margin.
