This paper assesses two popular features and their first order dynamic form (\Delta) in terms of their sensitivity to noise mis-match between the test and training conditions. We compare the robustness of individual features and features combined via LDA, using VQ models trained first with homogeneous data and then with mixed clean and single noise level data, all in the context of speaker identification. It is found that for a single feature RASTA-PLP and its \Delta form give better performance than that of MFCC and \Delta-MFCC under cross conditions, and generally combined features via LDA are better than the individual features in their immunity to noise. We show that single noise level training mixtures give good generalization between clean and the given noise level, and that an LDA feature of MFCC plus RASTA-PLP, with mixed training, can approach the performance of explicitly modeled undegraded and degraded speech. 1 INTRODUCTION Cepstral-based features, which are currently the...