  This paper deals the combination of nonlinear predictive models with
classical LPCC parameterization for speaker recognition. It is shown that the
combination of both a measure defined over LPCC coefficients and a measure
defined over predictive analysis residual signal gives rise to an improvement
over the classical method that considers only the LPCC coefficients. If the
residual signal is obtained from a linear prediction analysis, the improvement
is 2.63% (error rate drops from 6.31% to 3.68%) and if it is computed through a
nonlinear predictive neural nets based model, the improvement is 3.68%. An
efficient algorithm for reducing the computational burden is also proposed.
