Speech Emotion Recognition Using Deep Learning Models
Loading...
Date
2025-06-15
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Mohamed Boudiaf University of M'sila
Abstract
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