Adaptive artificial neural network based marathi speech database emotion recognition

Abstract

Nowadays, recognition of emotion from the speech signal is the wide spreading research topic since the speech signal is the quickest and natural approach to communicate with humans. A number of investigations have been progressed related to this topic. With the knowledge of many investigated model, this paper intends to recognize the emotions from the speech signal in a precise manner. To accomplish this, we intend to propose an adaptive learning architecture for the artificial neural network to learn the multimodal fusion of speech features. It results in a hybrid PSO-FF algorithm, which combines the features of both the PSO and FF towards training the network. The performance of the proposed recognition model has been analyzed by comparing it with the conventional methods in correspondence with varied performance measures like Accuracy, Sensitivity, Specificity, Precision, FPR, FNR, NPV, FDR, F1Score and MCC. Finally, the experimental analysis revealed that the proposed modal is 10.85% better than the conventional modals with respect to the accuracy for both the Marathi database and Benchmark database.