Deep Learning as Feature Encoding for Emotion Recognition

TL;DR

This paper explores using deep learning as a feature encoding method for emotion recognition, demonstrating it can outperform traditional techniques and improve fusion performance on the EmoDB dataset.

Contribution

It introduces a novel approach of using deep networks for feature encoding in emotion recognition, achieving state-of-the-art results.

Findings

Deep networks outperform traditional feature encoding methods.

Fusion of deep encoded features with other features enhances performance.

Achieves highest reported accuracy on EmoDB dataset.

Abstract

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.