An Enhanced Emotion Recognition Algorithm Using Pitch Correlogram, Deep Sparse Matrix Representation and Random Forest Classifier

This work presents an approach for text-independent and speaker-independent emotion recognition from speech in real application situations such as noisy and stressful talking conditions. We have incorporated a new way for feature extraction, representation, and noise reduction, replacing the frequently used cepstral features in the literature. The proposed algorithm is modeled as the combination of pitch-correlogram-based noise reduction pre-processing module, sparse-dense decomposition-based feature representation, and random forest classifier. The work is assessed in terms of efficiency and computational complexity using English and Arabic datasets corresponding to noisy and stressful talking conditions. Our system yields significant improvement in results in comparison with other techniques based on the same classifier model. The proposed network architecture achieves significant rise in performance correspond to the recent literature on benchmark datasets.