  While a great deal of work has been done on NLP approaches to lexical
semantic change detection, other aspects of language change have received less
attention from the NLP community. In this paper, we address the detection of
sound change through historical spelling. We propose that a sound change can be
captured by comparing the relative distance through time between their
distributions using PPMI character embeddings. We verify this hypothesis in
synthetic data and then test the method's ability to trace the well-known
historical change of lenition of plosives in Danish historical sources. We show
that the models are able to identify several of the changes under consideration
and to uncover meaningful contexts in which they appeared. The methodology has
the potential to contribute to the study of open questions such as the relative
chronology of sound shifts and their geographical distribution.
