Stereo-based Piecewise Linear Compensation for Environments (SPLICE) is a general framework for removing distortions from noisy speech cepstra. It contains a non-parametric model for cepstral corruption, which is learned from two channels of training data. We evaluate SPLICE on both the Aurora 2 and 3 tasks. These tasks consist of digit sequences in five European languages. Noise corruption is both synthetic (Aurora 2) and realistic (Aurora 3). For both the Aurora 2 and 3 tasks, we use the same training and testing procedure provided with the corpora. By holding the back-end constant, we ensure that any increase in word accuracy is due to our front-end processing techniques. In the Aurora 2 task, we achieve a 76.86% average decrease in word error rate with clean acoustic models, and an overall improvement of 62.63%. For the Aurora 3 task, we achieve a 75.06% average decrease in word error rate for the high-mismatch experiment, and an overall improvement of 47.19%.