Using context and phonetic features in models of etymological sound change

This paper presents a novel method for aligning etymological data, which models context-sensitive rules governing sound change, and utilizes phonetic features of the sounds. The goal is, for a given corpus of cognate sets, to find the best alignment at the sound level. We introduce an imputation procedure to compare the goodness of the resulting models, as well as the goodness of the data sets. We present evaluations to demonstrate that the new model yields improvements in performance, compared to previously reported models.