Deep Learning for Speech Emotion Recognition: A CNN Approach Utilizing Mel Spectrograms

This paper explores the application of Convolutional Neural Networks CNNs for classifying emotions in speech through Mel Spectrogram representations of audio files. Traditional methods such as Gaussian Mixture Models and Hidden Markov Models have proven insufficient for practical deployment, prompting a shift towards deep learning techniques. By transforming audio data into a visual format, the CNN model autonomously learns to identify intricate patterns, enhancing classification accuracy. The developed model is integrated into a user-friendly graphical interface, facilitating realtime predictions and potential applications in educational environments. The study aims to advance the understanding of deep learning in speech emotion recognition, assess the models feasibility, and contribute to the integration of technology in learning contexts