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In this paper, we present a novel deep multimodal framework to predict humanemotions based on sentence-level spoken language. Our architecture has twodistinctive characteristics. First, it extracts the high-level features fromboth text and audio via a hybrid deep multimodal structure, which considers thespatial information from text, temporal information from audio, and high-levelassociations from low-level handcrafted features. Second, we fuse all featuresby using a three-layer deep neural network to learn the correlations acrossmodalities and train the feature extraction and fusion modules together,allowing optimal global fine-tuning of the entire structure. We evaluated theproposed framework on the IEMOCAP dataset. Our result shows promisingperformance, achieving 60.4% in weighted accuracy for five emotion categories.

Deep Multimodal Learning for Emotion Recognition in Spoken Language