Motivated by the fact that the pronunciation of a name may be influenced by its language of origin, we present methods to improve pronunciation prediction of proper names using word origin information. We train grapheme-to-phoneme (G2P) models on language-specific data sets and interpolate the outputs. We perform experiments on US surnames, a data set where word origin variation occurs naturally. Our methods can be used with any G2P algorithm that outputs posterior probabilities of phoneme sequences for a given word.