Artificial Emotional Intelligence: Conventional and deep learning approach

Artificial intelligence substantially changes the global world, influencing technologies, machines, and objects in various encouraging aspects nowadays; emotion recognition is also one of them. This paper describes a signif-icant contribution of emotion recognition by applying conventional and deep learning methodologies by focusing on limitations and demanding challenges. It also intends to explore the comparative study on recently applied machine learning and deep learning-based algorithms, which provide the best accuracy rates to recognize emotions. This Comparative study consists of different feature extractions, classifier models, and datasets that recognize the emotions within a facial image, speech, and non-verbal communication and describes their features and principles for future research work. We have shown the balancing accuracy, and efficiency of using hybrid classification techniques briefly explained in Speech emotion recognition. This review study would be more beneficial in enhancing automated decision-making services in various customer-based industries and observing patients in the health care sector, industries, public sectors, private sectors, and production firms.

Automated summary

This paper discusses the application of emotion recognition in the field of artificial intelligence, focusing on its limitations and challenges. It compares the latest machine learning and deep learning algorithms and evaluates their accuracy and effectiveness in emotion recognition. The study demonstrates that hybrid classification techniques achieve a balance between accuracy and efficiency in speech emotion recognition.