Deep Neural Network Approaches for Emotion Recognition in Human–Computer Interaction

Abstract

Emotion recognition has become a pivotal research domain in Human–Computer Interaction (HCI), as modern interactive systems increasingly aim to respond not only to explicit user commands but also to implicit emotional cues. Understanding human emotions allows intelligent systems to adapt their behaviour, enhance user experience, and support applications such as intelligent tutoring systems, healthcare monitoring, customer service automation, and social robotics. Traditional emotion recognition methods relied heavily on handcrafted features and shallow machine learning algorithms, which struggled with high-dimensional data, environmental variability, and real-time performance constraints. Recent advances in deep learning have significantly transformed emotion recognition research. Deep Neural Networks (DNNs), including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and hybrid architectures, have demonstrated superior capability in learning hierarchical and discriminative representations directly from raw multimodal data. These models have enabled more accurate recognition of emotions from facial expressions, speech signals, textual inputs, and physiological signals. This paper presents an in-depth study of deep neural network approaches for emotion recognition in HCI. It systematically reviews existing literature, discusses methodological frameworks, explores tools and technologies used for implementation, and analyses experimental results obtained from deep learning-based emotion recognition systems. Special emphasis is placed on multimodal emotion recognition and hybrid deep architectures, which have shown substantial improvements over unimodal systems. The study also highlights key challenges such as dataset bias, cultural dependency of emotions, real-time deployment issues, and ethical considerations. Finally, the paper outlines future research directions focusing on explainable artificial intelligence, edge-based emotion recognition, and emotionally adaptive intelligent interfaces.