AFFECTIVE COMPUTING TECHNIQUES FOR HUMAN–MACHINE INTERACTION

Abstract: Affective computing is an emerging domain of artificial intelligence that focuses on enabling machines to recognize, interpret, and respond to human emotions. With the rapid advancement of intelligent systems such as chatbots, virtual assistants, and service robots, the ability to understand human emotions has become essential for improving interaction quality and user satisfaction. This paper presents a comprehensive survey and comparative analysis of various emotion recognition techniques, including facial expression recognition, speech emotion recognition, and text-based sentiment analysis. Deep learning models such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM) networks are widely used for emotion detection tasks. Furthermore, multimodal approaches that combine multiple data sources are analyzed for their effectiveness in reducing ambiguity and improving accuracy. The study also discusses key challenges such as real-time processing, data bias, and privacy concerns. Finally, the paper highlights future research directions for developing efficient and human-centric emotion- aware systems.

Keywords: Affective Computing, Emotion Recognition, Deep Learning, CNN, LSTM, Multimodal Systems, Human– Machine Interaction