Deep Learning as Feature Encoding for Emotion Recognition
This work addresses emotion recognition for applications like human-computer interaction, but it appears incremental as it applies existing deep learning methods to a known dataset.
The paper tackled emotion recognition by using deep learning networks as feature encoding techniques for low-level descriptors on the EmoDB dataset, achieving the highest performance reported in the literature.
Deep learning is popular as an end-to-end framework extracting the prominent features and performing the classification also. In this paper, we extensively investigate deep networks as an alternate to feature encoding technique of low level descriptors for emotion recognition on the benchmark EmoDB dataset. Fusion performance with such obtained encoded features with other available features is also investigated. Highest performance to date in the literature is observed.