  In this paper we propose a new parameterization algorithm based on nonlinear
prediction, which is an extension of the classical LPC parameters. The
parameters performances are estimated by two different methods: the
Arithmetic-Harmonic Sphericity (AHS) and the Auto-Regressive Vector Model
(ARVM). Two different methods are proposed for the parameterization based on
the Neural Predictive Coding (NPC): classical neural networks initialization
and linear initialization. We applied these two parameters to speaker
identification. The fist parameters obtained smaller rates. We show for the
first parameters how they can be combined with the classical parameters (LPCC,
MFCC, etc.) in order to improve the results of only one classical
parameterization (MFCC provides 97.55% and MFCC+NPC 98.78%). For the linear
initialization, we obtain 100% which is great improvement. This study opens a
new way towards different parameterization schemes that offer better accuracy
on speaker recognition tasks.
