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

V alues taken from SER Classifier notebook. Next, the model was tested on unique audio from myself, family, and friends. Surprisingly, it performed well, especially with negative emotions. For example, it correctly predicted male anger with over 90% accuracy, often distinguishing it from other emotions like male disgust, female anger, and male sadness. An interesting test involved a friend with Asperger's syndrome, who struggles with recognizing emotions. While the model's accuracy seemed initially low, further analysis revealed that her own perception of emotions was misaligned with the model's predictions, which were actually more accurate. Finally, the model was tested on German and Swiss German audio, where it performed well in predicting anger, sadness, and disgust. However, it made some errors with positive emotions. In all cases of failure, the target emotion remained within the top 5 predicted classes, demonstrating the model's robustness.