Comprehensive survey on multimodal emotion detection with advances, challenges, and future directions in pairwise conversations.

Emotion recognition is crucial for fostering effective interpersonal relationships and plays a significant role in affective interpersonal communications. Detecting emotions in pairwise communication has profound implications for enhancing human-computer interaction, advancing medical applications, and improving interpersonal communication analysis. This paper identifies the key challenges in emotion detection including the development of data fusion techniques, create diverse and high-quality datasets, improve model interpretability, and advance real-time processing capabilities. Current multi-modal emotion detection research predominantly focuses on emotions such as sadness, happiness, neutrality, anger, and disgust. Cognitive science presents a broader perspective, emphasizing the interaction between physiological responses and cognitive evaluations in the emotion recognition process. However, the analysis of micro-level expressions remains underdeveloped due to the lack of real-world datasets and effective fusion techniques. This review paper critically examines various fusion methodologies for integrating multiple modalities and presents a comparative study of deep learning approaches for emotion detection across these modalities. Advanced deep learning techniques, including attention mechanisms, transformers, and large language models (LLMs) explored in depth. This research serves as a benchmark for scholars and professionals in the field of affective computing and offers broader implications across technology, healthcare, education, and social sciences.