Emotion Speech Recognition using Deep Learning

Emotion Speech Recognition (ESR) is recognizing the formation and change of speaker’s emotional state from his/her speech signal. The main purpose of this field is to produce a convenient system that is able to effortlessly communicate and interact with humans. The reliability of the current speech emotion recognition systems is far from being achieved. However, this is a challenging task due to the gap between acoustic features and human emotions, which relies strongly on the discriminative acoustic features extracted for a given recognition task. Deep learning techniques have been recently proposed as an alternative to traditional techniques in ESR. In this paper, an overview of Deep Learning techniques that could be used in Emotional Speech recognition is presented. Different extracted features like MFCC as well as feature classifications methods including HMM, GMM, LTSTM and ANN have been discussed. In addition, the review covers databases used, emotions extracted, and contributions made toward ESR.