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Emotion recognition has been an active research area with both wide applications and big challenges. This paper presents our effort for the Audio/Visual Emotion Challenge (AVEC2015), whose goal is to explore utilizing audio, visual and physiological signals to continuously predict the value of the emotion dimensions (arousal and valence). Our system applies the Recurrent Neural Networks (RNN) to model temporal information. We explore various aspects to improve the prediction performancedoi:10.1145/2808196.2811638 dblp:conf/mm/ChenJ15 fatcat:rxqecowahfalln6bcmo77x6ryq