Abstract

The challenges of speech emotion recognition (SER) are examined in this study, highlighting limitations in conventional methods and the need for a deeper understanding. The review addresses the complexity of emotion definition and feature representation, crucial for accurate recognition. In essence, the research contributes to enhancing SER through a comprehensive exploration of machine learning techniques. A complicated area of affective computing, speech-to-text analysis (SER) requires a detailed examination of the emotions expressed in voice signals. Within the last decade, SER has become integral to Human-Computer Interaction and advanced speech processing systems. It identifies research gaps, particularly in fine-grained emotion classification essential for applications like psychological counseling. Addressing the uncertainty in emotion definition and the complexity of feature representation, the review paper advocates a deep dive into machine learning techniques to enhance SER's accuracy and effectiveness. This contribution aims to advance the understanding of speech emotion recognition, providing valuable insights for researchers and practitioners in this evolving field.