The paper presents two finite-state methods which can be used for aligning pairs of cognate words or sets of different allomorphs of stems. Both methods use weighted finite-state machines for choosing the best alternative. Individual letter or phoneme correspondences can be weighted according to various principles, e.g. using distinctive features. The comparison of just two forms at a time is simple, so that method is easier to refine to include context conditions. Both methods are language independent and could be tuned for and applied to several types of languages for producing gold standard data. The algorithms were implemented using the HFST finite-state library from short Python programs. The paper demonstrates that the solving of some non-trivial problems has become easier and accessible for a wider range of scholars.Peer reviewe
