An unsupervised, sentence-level, discriminative, HMM adaptation algorithm based on silence- speech classification is presented. Silence and speech regions are determined either using an end- pointer or using the segmentation obtained from the recognizer in a first pass. A unsupervised discriminative training procedure using the gradient descent algorithm, with N-best competing strings with word insertions is then used to improve the discrimination between silence and speech. Experiments on connected digits show about 40-80 % reduction in insertion errors, a small amount of reduction in substitution errors, and a negligible effect on deletion errors. In addition, experiments on noisy speech showed about 70% word error rate reduction, thus demonstrating the robustness of the proposed adaptation technique.