Affective Speech Processing Using MFCC

Deep learning has made emotion detection a core field that provides valuable insights into sentiment analysis, affective computing, and human-computer interaction. This study offers a thorough analysis of the most recent methods, approaches, and difficulties in emotion recognition using natural language processing. We start by investigating the theoretical foundations of emotions and how they emerge in language. Next, we examine the many ways used in mfcc-based emotion recognition, from deep learning models to lexicon-based strategies. Additionally, we go over how important it is to use language features, sentiment analysis methods, and multimodal data integration to increase the accuracy and robustness of systems that identify emotions. Additionally, this study looks at the real-world uses of Deep learning-based emotion recognition in a variety of fields, such as social media analysis, healthcare, and education.