Speaker recognition using residual signal of linear and nonlinear prediction models
Marcos Faúndez-Zanuy, Daniel Rodriguez-Porcheron · 1998
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%.