Accurate Speech Emotion Recognition by using Brain Inspired Decision Making Spiking Neural Network
A portion of speech recognition is taken away by emotion recognition which is a smart update and it is necessary for its gain massively. Feature selection is an indispensable stage among the furtherance of various schemes in order to implement the classification of sentiments in speaking. The communication among features prompted from the alike audio origin has been rarely deliberated at present, which might yield terminated features and cause an upswing in the computational costs. To resolve these defects the deep learning based feature extraction technique is used. An incredible modernization in speech recognition in recent years incorporates machine learning techniques with a deep structure for feature extraction. In this paper, the speech signal obtained from the SAVEE database is used as an input for a deep belief network. In order to perform pre training in the network, the layer wise rapacious feature extraction tactic is implemented and by using systematic samples, the smearing back propagation method is accomplished for attaining fine tuning. Brain inspired decision making spiking neural network (SNNs) is used to recognize different emotions but training by deep SNNs remains a challenge, but it improves the determination of the result. In order to enhance the parameters of SNNs, a social ski driver (SSD) evolutionary optimization algorithm is used. The results of the SNN SSD algorithm are related to artificial neural networks and long short term memory with different emotions to refine the classification for authorization.