This paper compares different measures of graphemic similarity applied to the task of bilingual lexicon induction between a Swiss German dialect and Standard German. The measures have been adapted to this particular language pair by training stochastic transducers with the Expectation-Maximisation algorithm or by using hand-made transduction rules. These adaptive metrics show up to 11% F-measure improvement over a static metric like Levenshtein distance.