Build a Model for Speech Emotion Recognition using Gaussian Mixture Model (GMM)

Emotions play a profoundly influential role in the human mental experience. However, in the present generation, both humans and machines lack the ability to fully perceive and express emotions. The primary goal of Speech Emotion Recognition (SER) is to enhance the man-machine interface. SER involves extracting emotional states from datasets obtained from speech signals. The system's ultimate goal is to achieve high accuracy in detecting emotions, including angry, calm, fearful, happy, and sad emotions. This is accomplished by employing suitable techniques for feature extraction and machine learning algorithms, such as the Gaussian Mixture Model (GMM). The implementation of this model has significant implications for improving humanmachine interactions and enhancing various applications that necessitate emotion recognition, including healthcare, education, marketing, and advertising. As a result, model is adaptable to different emotions and its overall effectiveness make it a valuable tool in this domain.