Decoding Emotions: Machine Learning Approach to Speech Emotion Recognition

Abstract

Speech Emotion Recognition (SER) stands at the forefront of human-computer interaction, offering profound implications for fields such as healthcare, education, and entertainment. This project report delves into the application of Machine Learning (ML) techniques for SER, aiming to discern the emotional content from speech signals. The report begins with an overview of the significance of SER in various domains, emphasizing the need for accurate and robust emotion detection systems. Following this,a detailed exploration of the methodologies employed in SER is presented, encompassing feature extraction techniques, classification algorithms, and model evaluation metrics. In the implementation phase, diverse ML algorithms such as Support Vector Machine (SVM), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) are employed to classify emotional states from speech data. Feature sets including prosodic features, spectral features, and deep learning-based representations are extracted from the speech signals to capture nuanced emotional cues.