Nonlinear system identification for speech model using linear predictive coefficients based radial basis function

Abstract

In this paper, the authors have proposed a Linear Predictive coefficient-based Radial basis function neural network model (LPC-RBF) to solve the system identification problem. A recorded speech signal is considered to develop the model. The objective is to develop a machine learning model with the LPC values of the speech signal. The parameters of a speech signal are directly estimated using linear predictive coefficients. The proposed model is considered as the ARX model and trained with the LPC value of the speech signal. The comparison results are shown in the table.The performance is evaluated through mean square error (MSE). From the result analysis, the superiority of the proposed model is analyzed. The proposed model archives the best final MSE value 0.0027, as compared to the basic RBF model. From the comparison table, it can be seen that LPC-RBF has better performance as compared with the RBF model.