Efficient adaptive algorithm for modeling a speech signal

This paper presents a time-domain prediction error method based on Newton algorithm identification for the identification of auto regressive moving average (ARMA) models of linear structural systems. The proposed structural identification method can incorporate as much as possible structural information known a priori into the structural identification process to improve the identification accuracy. This method incorporates a recursive procedure that makes successive corrections in determining, a linear mathematical model, based on the data of observation, to represent the system considered. To avoid the practical difficulty often associated with bias prediction error measurement, a stochastic Newton method is proposed. The numerical results show that the proposed method is capable to obtain the minimal standard deviation of error prediction and the estimated parameters are closer with actual values. An application of a speech signal is provided then to identify the parameters of AR model corresponding to the speech signal.