“…The testing is done on clean and noisy conditions to test the robustness of the proposed feature extraction algorithm, 4 noise types are chosen from the Noisex-92 noise dataset (babble, factory 1, pink and white) that are added to the test utterances with SNR levels 0, 5, 10 and 15 db. The results showed that the proposed features outperforms baseline features (PNCC and GFCC) and other proposed works Islam et al, in 2016, Korba et al, in 2018, Guo et al, in 2017and Ajgou et al in 2016, so it's a promising approach for extracting robust features and increasing speaker identification rate.…”