Emotion Recognition from Speech Signals Using DCNN with Hybrid GA-GWO Algorithm

Abstract

: In recent days, from the speech signal the recognition of emotion is considered as an extensive advanced investigation subject because the speech signal is considered as the rapid and natural method to communicate with humans. Numerous examinations have been progressed related to this topic. This paper develops the emotions recognition from the speech signal in an accurate way, with the knowledge of numerous examined models. Therefore, to study the multimodal fusion of speech features, a Deep Convolutional Neural Network model is proposed. Moreover, the hybrid Genetic Algorithm (GA)-Grey Wolf Optimization (GWO) algorithm is presented that is the combination of both the GA and GWO technique features towards training the network. Finally, the developed recognition model is verified and compared with the existing techniques in correlation with diverse performance measures such as Accuracy, Sensitivity, Precision, Specificity, False Positive Rate (FPR), False Discovery Rate (FDR), False Negative Rate (FNR), F1Score, Negative Predictive Value (NPV)