In this paper we propose a method for the automatic decipherment of lost languages. Given a non-parallel corpus in a known related language, our model produces both alphabetic mappings and translations of words into their corresponding cognates. We employ a non-parametric Bayesian framework to simultaneously capture both low-level character mappings and high-level morphemic correspondences. This formulation enables us to encode some of the linguistic intuitions that have guided human decipherers. When applied to the ancient Semitic language Ugaritic, the model correctly maps nearly all letters to their Hebrew counterparts, and deduces the correct Hebrew cognate for over half of the Ugaritic words which have cognates in Hebrew.See here, for a little background on the scripts.
They apparently have plans to try it next on Etruscan. That's going to be a serious challenge, but this could be cool.
Who knows, maybe the Voinych manuscript is next!