In this paper we propose a new feature extraction algorithm based on nonlinear prediction: the Neural Predictive Coding model which is an extension of the classical LPC one. This model is applied to speaker verification by the Arithmetic-Harmonic Sphericity (AHS) method. Two different initialization methods are proposed for the coding method based on the Neural Predictive Coding (NPC): classical neural networks initialization and linear initialization. The first model obtains smaller rates. For the linear initialization, we obtain significant improvement in comparison to the most used methods (LPCC, MFCC). This study opens a new way towards different feature extraction schemes that offers better accuracy on speaker recognition tasks.