Artificial Intelligence in Speech Emotion Detection: Trends, Challenges, and Future Directions

Abstract

Speech Emotion Detection (SED) has become a pivotal component in the development of emotionally aware artificial intelligence systems. This paper presents a comprehensive review of recent advancements in the field, focusing on signal processing techniques, machine learning and deep learning approaches, real-time implementation, and multimodal integration. The study highlights the critical role of feature extraction and classification methods in improving emotion recognition accuracy and robustness. Additionally, it discusses emerging trends such as personalization, explainable AI (XAI), and adaptation to noisy and culturally diverse environments. Ethical considerations and legal implications surrounding emotion-aware systems are examined, along with practical applications in healthcare, education, customer support, and entertainment. The review concludes by outlining the unresolved challenges and proposing future research directions to bridge existing gaps and enable more human-centric and trustworthy emotion recognition technologies.