  This Paper discusses the usefulness of the residual signal for speaker
recognition. It is shown that the combination of both a measure defined over
LPCC coefficients and a measure defined over the energy of the residual signal
gives rise to an improvement over the classical method which 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%.
