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This letter addresses the issue of frequency warping-based speaker normalization in noisy acoustic environments. Techniques are developed for improving the robustness of localized estimates of frequency warping transformations that are applied to individual observation vectors. It is shown that automatic speech recognition (ASR) performance can be improved by using speaker class-dependent distributions characterizing frequency warping transformations associated with individual hidden Markov model states. The effect of these techniques is demonstrated over a range of noise conditions on the Aurora 2 speech corpus.