Towards Robust Arabic Speech Emotion Recognition with Deep Learning
Researchers have developed a new deep learning framework to improve Arabic speech emotion recognition, a task that has been historically challenging due to dialectal diversity and limited datasets. The study compared three architectures: CNN-LSTM, CNN-Transformer, and a fine-tuned wav2vec 2.0 model. Experiments showed that the CNN-Transformer architecture achieved a 98.1 percent accuracy, outperforming the other models by effectively combining spectral feature extraction with global context modeling. AI
IMPACT Improves accuracy in a low-resource language domain, potentially enabling new applications in cross-cultural AI.