Hybrid CNN-Transformer Model Achieves 97.8% Accuracy in Arabic Speech Emotion Recognition

Researchers developed a hybrid CNN-Transformer architecture for Arabic speech emotion recognition, addressing a gap in non-English language AI. The model combined convolutional feature extraction with Transformer attention mechanisms, achieving 97.8% accuracy on the EYASE Egyptian Arabic dataset—demonstrating viability of advanced AI techniques for low-resource languages.