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

arXiv: https://arxiv.org/abs/2303.17932 · HTML: https://mlsanity.com/paper/2303.17932/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 60 references that are in the map.
- First posted on arXiv 31 Mar 2023.

## Builds on (most cited first, 1 of 1)

- [A New Framework for Fast Automated Phonological Reconstruction Using Trimmed Alignments and Sound Correspondence Patterns](https://mlsanity.com/paper/2204.04619/) (2022)

## Built on by (most cited first, 5 of 5)

- [Are Sounds Sound for Phylogenetic Reconstruction?](https://mlsanity.com/paper/2402.02807/) (2024)
- [A Computational Model for the Assessment of Mutual Intelligibility Among Closely Related Languages](https://mlsanity.com/paper/2402.02915/) (2024)
- [Representing and Computing Uncertainty in Phonological Reconstruction](https://mlsanity.com/paper/2310.12727/) (2023)
- [From Isolates to Families: Using Neural Networks for Automated Language Affiliation](https://mlsanity.com/paper/2502.11688/) (2025)
- [Using Correspondence Patterns to Identify Irregular Words in Cognate sets Through Leave-One-Out Validation](https://mlsanity.com/paper/2602.02221/) (2026)

## Abstract (arXiv)

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.