Robust discrimination in automatic speaker identification

The development of a robust discrimination technique that does not eliminate outliers or perform robust estimation of model parameters but instead applies robustness techniques in the discrimination process is presented. Results showing the performances of robust and nonrobust methods are presented and compared. The database used for the experiments contains 16 speakers and the speech for each of the 16 speakers was recorded on 10 different occasions over long-distance telephone circuits