Title: Evaluating deep learning architectures for speech emotion recognition
Authors: Haytham M Fayek, Margaret Lech, Lawrence Cavedon
Published: 2018-08-01
Link: https://www.sciencedirect.com/science/article/abs/pii/S089360801730059X
Abstract
Most existing Speech Emotion Recognition (SER) systems rely on turn-wise processing, which aims at recognizing emotions from complete utterances and an overly-complicated pipeline marred by many preprocessing steps and hand-engineered features. To overcome both drawbacks, we propose a real-time SER system based on end-to-end deep learning. Namely, a Deep Neural Network (DNN) that recognizes emotions from a one second frame of raw speech spectrograms is presented and investigated. This is achievable due to a deep hierarchical architecture, data augmentation, and sensible regularization. Promising results are reported on two databases which are the eNTERFACE database and the Surrey Audio-Visual Expressed Emotion (SAVEE) database.