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The integration of information across multiple modalities and across time isa promising way to enhance the emotion recognition performance of affectivesystems. Much previous work has focused on instantaneous emotion recognition.The 2018 One-Minute Gradual-Emotion Recognition (OMG-Emotion) challenge, whichwas held in conjunction with the IEEE World Congress on ComputationalIntelligence, encouraged participants to address long-term emotion recognitionby integrating cues from multiple modalities, including facial expression,audio and language. Intuitively, a multi-modal inference network should be ableto leverage information from each modality and their correlations to improverecognition over that achievable by a single modality network. We describe herea multi-modal neural architecture that integrates visual information over timeusing an LSTM, and combines it with utterance level audio and text cues torecognize human sentiment from multimodal clips. Our model outperforms theunimodal baseline, achieving the concordance correlation coefficients (CCC) of0.400 on the arousal task, and 0.353 on the valence task.

Multimodal Utterance-level Affect Analysis using Visual, Audio and Text Features