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Data imputation approaches for robust automatic speech recognition reconstruct noise corrupted spectral information by exploiting prior knowledge of the relationship between tar get speech and background characterized by spectrographic masks. Most of these approaches operate without considering the temporal or spectral trajectories of the spectral components. Discrete wavelet transform (DWT) based filter banks are investigated here for spectrogram reconstruction to address the well known importance of preserving spectro temporal modulation characteristics in the speech spectrum. A novel approach is presented for propagating prior spectro graphic mask probabilities to serve as oracle information for thresholding coefficients in a wavelet de-noising scenario. The results of an experimental study are presented to demonstrate the performance of DWT based data imputation relative to a well known MMSE based approach on the Aurora 2 noisy speech recognition task.