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The performance of speech recognition systems depends on consistent quality of the speech features across variable environmental conditions encountered during training and evaluation. This paper presents a kernel-based nonlinear predictive coding procedure that yields speech features which are robust to nonstationary noise contaminating the speech signal. Features maximally insensitive to additive noise are obtained by growth transformation of regression functions that span a reproducing kerneldoi:10.1109/tasl.2007.899285 fatcat:76e4ivmhqjhphn4dqspswdpkne