This paper describes recent improvements to SPLICE, Stereobased Piecewise Linear Compensation for Environments, which produces an estimate of cepstrum of undistorted speech given the observed cepstrum of distorted speech. For distributed speech recognition applications, SPLICE can be placed at the server, thus limiting the processing that would take place at the client. We evaluated this algorithm on the Aurora2 task, which consists of digit sequences within the TIDigits database that have been digitally corrupted by passing them through a linear filter and/or by adding different types of realistic noises at SNRs ranging from 20dB to -5dB. On set A data, for which matched training data is available, we achieved a 66% decrease in word error rate over the baseline system with clean models. This preliminary result is of practical significance because in a server implementation, new noise conditions can be added as they are identified once the service is running.