A Novel S-LDA Features for Automatic Emotion Recognition from Speech using 1-D CNN

Abstract

Emotions are explicit and serious mental activities, which find expression in speech, body gestures and facial features, etc. Speech is a fast, effective and the most convenient mode of human communication. Hence, speech has become the most researched modality in Automatic Emotion Recognition (AER). To extract the most discriminative and robust features from speech for Automatic Emotion Recognition (AER) recognition has yet remained a challenge. This paper, proposes a new algorithm named Shifted Linear Discriminant Analysis (S-LDA) to extract modified features from static low-level features like Mel-Frequency Cepstral Coefficients (MFCC) and Pitch. Further 1-D Convolution Neural Network (CNN) was applied to these modified features for extracting high-level features for AER. The performance evaluation of classification task for the proposed techniques has been carried out on the three standard databases: Berlin EMO-DB emotional speech database, Surrey Audio-Visual Expressed Emotion (SAVEE) database and eNTERFACE database. The proposed technique has shown to outperform the results obtained using state of the art techniques. The results shows that the best accuracy obtained for AER using the eNTERFACE database is 86.41%, on the Berlin database is 99.59% and with SAVEE database is 99.57%.