Effective Feature Selection in Speech Emotion Recognition Systems using Generative Adversarial Networks

Abstract

Thus far, it has been unknown whether feature selection methods succeed in increasing the efficiency of speech-emotion recognition systems. This article discusses and evaluates feature selection for data augmentation purposes in a speech emotion recognition system. This study performed the experiments using Python and on four common databases: EMO-DB, eNTERFACE05, SAVEE, and IEMOCAP. Data analysis was conducted on all four databases for five emotions: sadness, fear, anger, happiness, and neutral. A support vector machine was used to classify emotions. We also used a generative adversarial network to augment data and two feature selection networks including Fisher and Linear Discriminant Analysis algorithms. In two steps and with the feedback from the classification network, we could bring the speech emotion recognition to an optimal point in sample number and feature vector dimensions. The results showed that using Linear Discriminant Analysis and the Fisher method simultaneously in the generative adversarial networks can remove redundant and irrelevant features while preserving features with important emotional information for classification. The results obtained from the proposed method were compared with that of recent studies. The proposed method was able to achieve 86.32\% accuracy in the Berlin Database of Emotional Speech.