Speech Emotion Recognition with deep learning

This paper proposes an emotion recognition system based on speech signals in two-stage approach, namely feature extraction and classification engine. Firstly, two sets of feature are investigated which are: the first one, we extract an 42-dimensional vector of audio features including 39 coefficients of Mel Frequency Cepstral Coefficients (MFCC), Zero Crossing Rate(ZCR), Harmonic to Noise Rate (HNR) and Teager Energy Operator (TEO). And the second one, we propose the use of the method Auto-Encoder for the selection of pertinent parameters from the parameters previously extracted. Secondly, we use the Support Vector Machines (SVM) as a classifier method. Experiments are conducted on the Ryerson Multimedia Laboratory (RML).