We present a semi-supervised algorithm for rescoring the output of a speech keyword search (KWS) system. Conventional loss functions such as squared-error and logistic loss are not suitable for optimizing the commonly-used KWS term-weighted value (TWV) performance metric. We derive a novel concave modified logistic log-likelihood function which lower-bounds TWV. We then use a manifold-regularized kernel classifier that maximizes this lower-bound. A manifold regularization term in our objective function uses available unlabeled speech data and makes our approach semi-supervised. This term is particularly useful for KWS in low-resource languages and ensures that the predicted keyword confidence scores are smooth on a low-dimensional manifold in the feature space. We conduct KWS experiments on the IARPA Babel Vietnamese task and show performance improvements in terms of the maximum TWV (MTWV). Our estimated confidence score is complementary with respect to the ASR posterior score and gives MTWV improvement upon interpolation with it. © 2014 IEEE.