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Abstract

Human emotion recognition is becoming very important in human-computer interaction applications and stress management applications. In these applications, multimodal features extracted from various modalities like visual, speech, text, and biomedical sensor readings etc. to classify various emotions. For effective emotion recognition there has been extensive research on various modalities and their features which are to be considered for classification of emotions. This work explores multiple modalities and proposes a novel deep learning based multimodal features which are enhanced by using cross modality attention for recognition of basic human emotions The proposed solution provides higher discriminative ability in three cases of emotions like basic emotion, activation of emotion (positive, negative) and arousal of emotion (high, low).