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Band-limited speech represents one of the most challenging factors for robust speech recognition. This is especially true in supporting audio corpora from sources that have a range of conditions in spoken document retrieval requiring effective automatic speech recognition. The missing-feature reconstruction method has a problem when applied to band-limited speech reconstruction, since it assumes the observations in the unreliable regions are always greater than the latent original clean speech. The approach developed here depends only on reliable components to calculate the posterior probability to mitigate the problem. This study proposes an advanced method to effectively utilize the correlation information of the spectral components across time and frequency axes in an effort to increase the performance of missing-feature reconstruction in band-limited conditions. We employ an F1 area window and cutoff border window in order to include more knowledge on reliable components which are highly correlated with the cutoff frequency band. To detect the cutoff regions for missing-feature reconstruction, blind mask estimation is also presented, which employs the synthesized band-limited speech model without secondary training data. Experiments to evaluate the performance of the proposed methods are accomplished using the SPHINX3 speech recognition engine and the TIMIT corpus. Experimental results demonstrate that the proposed time-frequency (TF) correlation based missing-feature reconstruction method is significantly more effective in improving band-limited speech recognition accuracy. By employing the proposed TF-missing feature reconstruction method, we obtain up to 14.61% of average relative improvement in word error rate (WER) for four available bandwidths with cutoff frequencies 1.0, 1.5, 2.0, and 2.5 kHz, respectively, compared to earlier formulated methods. Experimental results on the National Gallery of the Spoken Word (NGSW) corpus also show the proposed method is- effective in improving band-limited speech recognition in real-life spoken document conditions.