Hierarchical Attention Transformers for Multimodal Human Emotion  Understanding

Udaya Kumar Nanubala , Dr.Pankaj Khairnar

Hierarchical Attention Transformers for Multimodal Human Emotion Understanding

Authors

Udaya Kumar Nanubala , Dr.Pankaj Khairnar

Keywords:

Multimodal Emotion Recognition, Transformer Networks, Deep Learning, Cross-Modal Attention, Text-Image-Speech Integration, Affective Computing, Artificial Intelligence, Vision Transformers, Speech Emotion Recognition, Context-Aware Learning

Abstract

Human emotions constitute a fundamental component of communication, cognition, andsocial interaction [7]. The increasing demand for emotionally intelligent artificial intelligencesystems has accelerated research in multimodal emotion recognition, where textual, visual, and auditory signals are jointly analyzed to infer emotional states

References

Vaswani, A., et al., “Attention Is All You Need,” NeurIPS, 2017.

Devlin, J., et al., “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” NAACL, 2019.

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Published

2024-10-15

How to Cite

Udaya Kumar Nanubala , Dr.Pankaj Khairnar. (2024). Hierarchical Attention Transformers for Multimodal Human Emotion Understanding. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 8687–8696. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/5431