Hybrid deep learning with optimal feature selection for speech emotion recognition using improved meta-heuristic algorithm

摘要

Speech emotion recognition is the crucial stream in emotional computing and also create few issues owing to its complication in processing. The efficiency of the acoustic methods and their speech features are improved using various existing methods. Yet, the conventional acoustic methods are not effective in handling speech emotion recognition because of their drawbacks. The main intend of this research is to implement a new speech emotion recognition using the hybrid deep learning model. Initially, few speech emotion recognition dataset is gathered from the public sources and is put forwarded for pre-processing using artifacts removal and filtering techniques. Then, the feature extraction of the speech signals is performed by the Mel-Frequency Cepstral Coefficients (MFCC), mel-scale spectrogram, tonal power, and spectral flux. In the aim of decreasing the feature size for boosting up the learning performance, for selecting the optimal feature is adopted by the Deer Hunting with Adaptive Search (DH-AS) algorithm. These optimal features are used for the emotion classification by the Hybrid Deep Learning (HDL) with “Deep Neural Network (DNN) and Recurrent Neural Network (RNN)”. These two networks are enhanced by the developed DH-AS, thus could reach high classification accuracy while classifying the emotions like “happy, sad, anger, fear, calm etc”. The performance of the suggested DH-AS-HDL correspondingly improves 3.15%, 5.37%, 4.25% and 4.81% better accuracy than the PSO-HDL, GWO-HDL, WOA-HDL and DHOA-HDL, when the learning rate as 85. The achieved results prove that the developed model obtains superior performance by evaluating its performance through various performance metrics.