A feature selection model for speech emotion recognition using clustering-based population generation with hybrid of equilibrium optimizer and atom search optimization algorithm

Speech plays an important role among the human communication and also a dominant source of medium for human computer interaction (HCI) to exchange information. Hence, it has always been an important research topic in the fields of Artificial Intelligence (AI) and Machine Learning (ML). However, in the traditional machine learning approach, when the dimension of the feature vector becomes quite large, it takes a huge amount of storage space and processing time for the learning algorithms. To address this problem, we have proposed a hybrid wrapper feature selection algorithm, called CEOAS, using clustering-based Equilibrium Optimizer (EO) and Atom Search Optimization (ASO) algorithm for recognizing different human emotions from speech signals. We have extracted Linear Prediction Coding (LPC) and Linear Predictive Cepstral Coefficient (LPCC) from the audio signals. Our proposed model helps to reduce the feature dimension as well as improves the classification accuracy of the learning model. The model has been evaluated on four standard benchmark datasets namely, SAVEE, EmoDB, RAVDESS, and IEMOCAP and impressive recognition accuracies of 98.01%, 98.72%, 84.62% and 74.25% respectively have been achieved which are better than many state-of-the-art algorithms.