  Sound correspondence patterns form the basis of cognate detection and
phonological reconstruction in historical language comparison. Methods for the
automatic inference of correspondence patterns from phonetically aligned
cognate sets have been proposed, but their application to multilingual
wordlists requires extremely well annotated datasets. Since annotation is
tedious and time consuming, it would be desirable to find ways to improve
aligned cognate data automatically. Taking inspiration from trimming techniques
in evolutionary biology, which improve alignments by excluding problematic
sites, we propose a workflow that trims phonetic alignments in comparative
linguistics prior to the inference of correspondence patterns. Testing these
techniques on a large standardized collection of ten datasets with expert
annotations from different language families, we find that the best trimming
technique substantially improves the overall consistency of the alignments. The
results show a clear increase in the proportion of frequent correspondence
patterns and words exhibiting regular cognate relations.
