Multimodal Fusion with Deep Neural Networks for Audio-Video Emotion Recognition
It addresses emotion recognition for applications like human-computer interaction, but is incremental as it builds on existing multimodal fusion methods.
This paper tackles emotion recognition by proposing a novel deep neural network for multimodal fusion of audio, video, and text, achieving higher Concordance Correlation Coefficient scores than state-of-the-art systems, with CCCs of 0.606, 0.534, and 0.170 for arousal, valence, and liking on a development dataset.
This paper presents a novel deep neural network (DNN) for multimodal fusion of audio, video and text modalities for emotion recognition. The proposed DNN architecture has independent and shared layers which aim to learn the representation for each modality, as well as the best combined representation to achieve the best prediction. Experimental results on the AVEC Sentiment Analysis in the Wild dataset indicate that the proposed DNN can achieve a higher level of Concordance Correlation Coefficient (CCC) than other state-of-the-art systems that perform early fusion of modalities at feature-level (i.e., concatenation) and late fusion at score-level (i.e., weighted average) fusion. The proposed DNN has achieved CCCs of 0.606, 0.534, and 0.170 on the development partition of the dataset for predicting arousal, valence and liking, respectively.