Emotion Intensity and Gender Detection via Speech and Facial Expressions


Human emotion detection has received increasing attention over the last decades for a variety of applications and systems. However, detecting the intensity of the expressed emotion has not been investigated as much as detecting the type of the expressed emotion. To fill this gap, we investigate the utility of different facial and speech features for emotion intensity detection. To this end, we designed different Deep Neural Network based models and applied them to the RAVDESS dataset. Obtained results show that speech signal features are better indicators of emotion intensity than facial features. However, in the absence of speech signals, finding emotion intensity by facial expressions is more accurate for males in comparison to females. The difference between the accuracy of emotion intensity detection for two genders motivated us to use speech signals for the gender detection task. The obtained results confirm that the proposed model achieves higher accuracy in emotion intensity detection and is more robust in gender detection than the state-of-the-art.

In Proceedings of the 31th Benelux Conference on Artificial Intelligence (BNAIC)