On the Usefulness of Linear and Nonlinear Prediction Residual Signals for Speaker Recognition

This paper compares the identification rates of a speaker recognition system using several parameterizations, with special emphasis on the residual signal obtained from linear and nonlinear predictive analysis. It is found that the residual signal is still useful even when using a high dimensional linear predictive analysis. On the other hand, it is shown that the residual signal of a nonlinear analysis contains less useful information, even for a prediction order of 10, than the linear residual signal. This shows the inability of the linear models to cope with nonlinear dependences present in speech signals, which are useful for recognition purposes.

• Type: Conference
• Year: 2007
• Where: NOLISP
• Authors: Marcos Faúndez-Zanuy
• Keywords: Linear Residual Signal, NOLISP 2007, Predictive Analysis, Residual Signal, Signal Processing