Multimodal fusion in speech emotion recognition: A comprehensive review of methods and technologies
Published in Engineering Applications of Artificial Intelligence, 2026 IF 9.0
Abstract (excerpt). Speech emotion recognition (SER) plays a crucial role in human–computer interaction, enhancing numerous applications such as virtual assistants, healthcare monitoring, and customer support by identifying and interpreting emotions conveyed through spoken language. While unimodal SER systems demonstrate notable simplicity and computational efficiency, excelling in extracting critical features like vocal prosody and linguistic content, there is a pressing need to improve their performance in challenging conditions, such as noisy environments and the handling of ambiguous expressions or incomplete information. These challenges underscore the necessity of transitioning to multimodal approaches, which integrate complementary data sources to achieve more robust and accurate emotion detection. With advancements in artificial intelligence, especially in neural networks and deep learning, many studies have …
Recommended citation: Nhut Minh Nguyen, Thanh Trung Nguyen, Phuong-Nam Tran, Chee Peng Lim, Nhat Truong Pham, Duc Ngoc Minh Dang. (2026). "Multimodal fusion in speech emotion recognition: A comprehensive review of methods and technologies." Engineering Applications of Artificial Intelligence, 163, 112624.
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