A hybrid meta-heuristic ensemble based classification technique speech emotion recognition

This research intends to propose a new unique and automated speech emotion reorganization model by going through. Initially, the obtained raw speech data is filtered using Butterworth filter and then the signals are segmented into frames. In addition, using the Convolutional Neural Network (CNN), the Spectrogram feature is recovered from the frames. After that, the extracted acoustic and Spectrogram feature is amalgamated to make a hybrid feature vector. Furthermore, the Enhanced-ReliefF (E-ReliefF) is utilized to choose the most important features from the hybrid feature vector. The speech emotion recognition phase is modelled with an ensemble-of-classifiers. The proposed EC consists of a Recurrent Neural Network (RNN), DBN, and an Artificial Neural Network (ANN). The ANN and DBN are trained with the hybrid feature vector. The results of DBN and ANN are fed into an optimized RNN, which will provide the final outcome corresponding to the emotions expressed in the input speech. Furthermore, a RNN weight is adjusted using a hybrid optimization technique to improve speech emotion categorization precision. Arithmetic Exploration updated Wildbeast Model (AEUWB) is a newly presented hybrid optimization model that combines two classic optimization models, namely Arithmetic Optimization Algorithm (AOA) and Wildebeest herd optimization (WHO). Moreover, a comparative analysis validates the projected model's (AEUWB+EC) effectiveness. Accordingly, the accuracy of the presented method is 12.3%, 9.2%, 7.2%, 15.45, 17.5% and 13.4% superior than the conventional models like LA+EC, SSO+EC, SSO+EC, WHO+EC, AOA+EC, respectively, at the 90th LP.