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This work proposes an approach for emotion recognition in conversation that leverages context modeling, knowledge enrichment, and multimodal (text and audio) learning based on a graph convolutional network (GCN). We first construct two distinctive graphs for modeling the contextual interaction and knowledge dynamic. We then introduce an affective lexicon into knowledge graph building to enrich the emotional polarity of each concept, that is the related knowledge of each token in an utterance. Then, we achieve a balance between the context and the affect-enriched knowledge by incorporating them into the new adjacency matrix construction of the GCN architecture, and teach them jointly with multiple modalities to effectively structure the semantics-sensitive and knowledge-sensitive contextual dependence of each conversation. Our model outperforms the state-of-the-art benchmarks by over 22.6% and 11% relative error reduction in terms of weighted-F1 on the IEMOCAP and MELD databases, respectively, demonstrating the superiority of our method in emotion recognition. [ABSTRACT FROM AUTHOR]

People usually express emotions through paralinguistic and linguistic information in speech. How to effectively integrate linguistic and paralinguistic information for emotion recognition is a challenge. Previous studies have adopted the bidirectional long short-term memory (BLSTM) network to extract acoustic and lexical representations followed by a concatenate layer, and this has become a common method. However, the interaction and influence between different modalities are difficult to promote using simple feature fusion for each sentence. In this article, we propose an implicitly aligned multimodal transformer fusion (IA-MMTF) framework based on acoustic features and text information. This model enables the two modalities to guide and complement each other when learning emotional representations. Thereafter, the weighed fusion is used to control the contributions of different modalities. Thus, we can obtain more complementary emotional representations. Experiments on the interactive emotional dyadic motion capture (IEMOCAP) database and multimodal emotionlines dataset (MELD) show that the proposed method outperforms the baseline BLSTM-based method. [ABSTRACT FROM AUTHOR]