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Sound correspondence patterns form the basis of cognate detection andphonological reconstruction in historical language comparison. Methods for theautomatic inference of correspondence patterns from phonetically alignedcognate sets have been proposed, but their application to multilingualwordlists requires extremely well annotated datasets. Since annotation istedious and time consuming, it would be desirable to find ways to improvealigned cognate data automatically. Taking inspiration from trimming techniquesin evolutionary biology, which improve alignments by excluding problematicsites, we propose a workflow that trims phonetic alignments in comparativelinguistics prior to the inference of correspondence patterns. Testing thesetechniques on a large standardized collection of ten datasets with expertannotations from different language families, we find that the best trimmingtechnique substantially improves the overall consistency of the alignments. Theresults show a clear increase in the proportion of frequent correspondencepatterns and words exhibiting regular cognate relations.

Trimming Phonetic Alignments Improves the Inference of Sound Correspondence Patterns from Multilingual Wordlists