A Comprehensive Review of Speech Emotion Recognition Systems

Speech Emotion Recognition (SER) has emerged as a pivotal technology in the realm of Human-Computer Interaction (HCI) and sophisticated speech processing applications. By extracting and classifying key features from speech signals, SER systems aim to identify a speaker’s emotional state. Despite significant progress, the fundamental differences in how humans and machines perceive and interpret emotional cues continue to pose challenges. These challenges often stem from the interdisciplinary nature of the field, which intersects speech processing, psychology, and user interface design. This review paper provides a structured and in-depth analysis of recent advancements in SER system architectures, methodologies, and feature extraction techniques. It consolidates key findings from the latest research to present a holistic understanding of the current landscape. Furthermore, the paper highlights existing research gaps and technical limitations, offering a foundation for future studies and the development of more robust and context-aware SER systems.