This paper considers the problem of signal clipping and stud- ies the performance of speech recognition in the presence of clipping. We present two novel algorithms for restoring the cor- rupted samples. Our first approach assumes that the original undistorted signal is band-limited and exploits this to iteratively reconstruct the original signal. We show that this approach has a monotonically non-increasing mean square error. In the second procedure we model the signal as an auto-regressive process and develop an estimation-maximization (EM) algorithm for esti- mating the model parameters. As a by product of the EM pro- cedure the signal is recovered. The effects of these methods on the accuracy of speech recognition are studied, and it is shown that over 4% relative word error rate reduction can be achieved on speech utterances with more than 0.5% clipped samples. Copyright © 2013 ISCA.