Speech Emotion Recognition using Machine Learning

Emotion recognition from speech is an emerging field within machine learning, aimed at improving human-computer interaction by enabling systems to understand and respond to human emotions. This paper presents a comprehensive study on Speech Emotion Recognition (SER) using machine learning techniques. We explore various feature extraction methods, including Mel-Frequency Cepstral Coefficients (MFCCs) and chroma features, to capture the emotional content from speech signals. The extracted features are utilized to train several machine learning models, including Support Vector Machines (SVM), Random Forests, and Convolutional Neural Networks (CNNs). Our approach is evaluated on publicly available datasets, and the performance is assessed using metrics such as accuracy, precision, recall, and F1-score. The experimental results demonstrate that our proposed models achieve significant improvements in emotion classification accuracy compared to existing methods. Additionally, we discuss the challenges and potential applications of SER in real-world scenarios, such as customer service, mental health monitoring, and virtual assistants. The findings of this study contribute to the advancement of robust and efficient SER systems, paving the way for more empathetic and intuitive human-computer interactions..