Speech Emotion Recognition Using Deep Learning Models - Rania, Agoune; Supervisor: Said, Gadri (Mohamed Boudiaf University of M'sila, 2025)
This project addresses the recognition of human emotions through speech using artificial intelligence. A CNN-based model was developed to classify emotions like happiness, anger, and sadness from features such as MFCC. Datasets like RAVDESS, SAVEE, and CASIA were combined to enhance diversity. Data augmentation techniques improved model generalization. The model achieved over 96% accuracy. A Flask-based web interface enables training, testing, and real time prediction. The system offers promising applications in mental health, education, and customer service. This work lays the groundwork for emotionally aware and intelligent interaction systems