Speech recognition front end noise removal algorithms have, in the past, estimated clean speech features from corrupted speech features. The accuracy of the noise removal process varies from frame to frame, and from dimension to dimension in the feature stream, due in part to the instantaneous SR of the input. In this paper, we show that localized knowledge of the accuracy of the noise removal process can be directly incorporated into the Gaussian evaluation within the decoder, to produce higher recognition accuracies. To prove this concept, we modify the SPLICE algorithm to output uncertainty information, and show that the combination of SPLICE with uncertainty decoding can remove 74.2% of the errors in a subset of the Aurora2 task.