# Automating Sound Change Prediction for Phylogenetic Inference: A Tukanoan Case Study

arXiv: https://arxiv.org/abs/2402.01582 · HTML: https://mlsanity.com/paper/2402.01582/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 41 references that are in the map.
- First posted on arXiv 2 Feb 2024.

## Builds on (most cited first, 3 of 3)

- [Where New Words Are Born: Distributional Semantic Analysis of Neologisms and Their Semantic Neighborhoods](https://mlsanity.com/paper/2001.07740/) (2020)
- [Are Automatic Methods for Cognate Detection Good Enough for Phylogenetic Reconstruction in Historical Linguistics?](https://mlsanity.com/paper/1804.05416/) (2018)
- [Quantifying Cognitive Factors in Lexical Decline](https://mlsanity.com/paper/2110.05775/) (2021)

## Built on by (most cited first, 4 of 4)

- [Programming by Examples Meets Historical Linguistics: A Large Language Model Based Approach to Sound Law Induction](https://mlsanity.com/paper/2501.16524/) (2025)
- [Can Large Language Models Code Like a Linguist?: A Case Study in Low Resource Sound Law Induction](https://mlsanity.com/paper/2406.12725/) (2024)
- [PILA: A Historical-Linguistic Dataset of Proto-Italic and Latin](https://mlsanity.com/paper/2404.16341/) (2024)
- [Exploring Sound Change Over Time: A Review of Computational and Human Perception](https://mlsanity.com/paper/2407.05092/) (2024)

## Abstract (arXiv)

We describe a set of new methods to partially automate linguistic phylogenetic inference given (1) cognate sets with their respective protoforms and sound laws, (2) a mapping from phones to their articulatory features and (3) a typological database of sound changes. We train a neural network on these sound change data to weight articulatory distances between phones and predict intermediate sound change steps between historical protoforms and their modern descendants, replacing a linguistic expert in part of a parsimony-based phylogenetic inference algorithm. In our best experiments on Tukanoan languages, this method produces trees with a Generalized Quartet Distance of 0.12 from a tree that used expert annotations, a significant improvement over other semi-automated baselines. We discuss potential benefits and drawbacks to our neural approach and parsimony-based tree prediction. We also experiment with a minimal generalization learner for automatic sound law induction, finding it comparably effective to sound laws from expert annotation. Our code is publicly available at https://github.com/cmu-llab/aiscp.