estimation of lpc coefficients using evolutionary algorithms

the vast use of linear prediction coefficients (lpc) in speech processing systems has intensified the importance of their accurate computation. this paper is concerned with computing lpc coefficients using evolutionary algorithms: genetic algorithm (ga), particle swarm optimization (pso), dif-ferential evolution (de) and particle swarm optimization with differentially perturbed velocity (pso-dv). in this method, evolutionary algorithms try to find the lpc coefficients which can predict the origi-nal signal with minimum prediction error. to this end, the fitness function is defined as the maximum prediction error in all evolutionary algorithms. the coefficients computed by these algorithms compared to coefficients obtained by traditional autocorrelation method in term of prediction accuracy. our results showed that coefficients obtained by evolutionary algorithms predict the original signal with less prediction error than autocorrelation methods. the maximum prediction error achieved by autocorrelation method, ga, pso, de and pso-dv are 0.35, 0.06, 0.02, 0.07 and 0.001, respectively. this shows that the hybrid algorithm, pso-dv, is superior to other algorithms in computing linear prediction coefficients.