  An effective way to increase the noise robustness of automatic speech
recognition is to label noisy speech features as either reliable or unreliable
(missing) prior to decoding, and to replace the missing ones by clean speech
estimates. We present a novel method to obtain such clean speech estimates.
Unlike previous imputation frameworks which work on a frame-by-frame basis, our
method focuses on exploiting information from a large time-context. Using a
sliding window approach, denoised speech representations are constructed using
a sparse representation of the reliable features in an overcomplete basis of
fixed-length exemplar fragments. We demonstrate the potential of our approach
with experiments on the AURORA-2 connected digit database.
