This paper proposes a multimodal emotion recognition system, VIsual Spoken Textual Additive Net (VISTANet), to classify emotions reflected by input containing image, speech, and text into discrete classes. A new interpretability technique, K-Average Additive exPlanation (KAAP), has been developed that identifies important visual, spoken, and textual features leading to predicting a particular emotion class. The VISTANet fuses information from image, speech, and text modalities using a hybrid of early and late fusion. It automatically adjusts the weights of their intermediate outputs while computing the weighted average. The KAAP technique computes the contribution of each modality and corresponding features toward predicting a particular emotion class. To mitigate the insufficiency of multimodal emotion datasets labeled with discrete emotion classes, we have constructed a large-scale IIT-R MMEmoRec dataset consisting of images, corresponding speech and text, and emotion labels (ángry,' 'happy,' 'hate,' and 'sad'). The VISTANet has resulted in 95.99% emotion recognition accuracy on the IIT-R MMEmoRec dataset using visual, audio, and textual modalities, outperforming when using any one or two modalities. The IIT-R MMEmoRec dataset can be accessed at https://github.com/MIntelligence-Group/MMEmoRec.
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