# Exploring Sound Change Over Time: A Review of Computational and Human Perception

arXiv: https://arxiv.org/abs/2407.05092 · HTML: https://mlsanity.com/paper/2407.05092/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 55 references that are in the map.
- First posted on arXiv 6 Jul 2024.

## Builds on (most cited first, 10 of 10)

- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change](https://mlsanity.com/paper/1605.09096/) (2016)
- [SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection](https://mlsanity.com/paper/2007.11464/) (2020)
- [Graph-based Clustering for Detecting Semantic Change Across Time and Languages](https://mlsanity.com/paper/2402.01025/) (2024)
- [Automating Sound Change Prediction for Phylogenetic Inference: A Tukanoan Case Study](https://mlsanity.com/paper/2402.01582/) (2024)
- [Letters From the Past: Modeling Historical Sound Change Through Diachronic Character Embeddings](https://mlsanity.com/paper/2205.08256/) (2022)
- [Are Sounds Sound for Phylogenetic Reconstruction?](https://mlsanity.com/paper/2402.02807/) (2024)
- [Syntactic Language Change in English and German: Metrics, Parsers, and Convergences](https://mlsanity.com/paper/2402.11549/) (2024)
- [Presence or Absence: Are Unknown Word Usages in Dictionaries?](https://mlsanity.com/paper/2406.00656/) (2024)
- [Detecting Syntactic Change Using a Neural Part-of-Speech Tagger](https://mlsanity.com/paper/1906.01661/) (2019)

## Abstract (arXiv)

Computational and human perception are often considered separate approaches for studying sound changes over time; few works have touched on the intersection of both. To fill this research gap, we provide a pioneering review contrasting computational with human perception from the perspectives of methods and tasks. Overall, computational approaches rely on computer-driven models to perceive historical sound changes on etymological datasets, while human approaches use listener-driven models to perceive ongoing sound changes on recording corpora. Despite their differences, both approaches complement each other on phonetic and acoustic levels, showing the potential to achieve a more comprehensive perception of sound change. Moreover, we call for a comparative study on the datasets used by both approaches to investigate the influence of historical sound changes on ongoing changes. Lastly, we discuss the applications of sound change in computational linguistics, and point out that perceiving sound change alone is insufficient, as many processes of language change are complex, with entangled changes at syntactic, semantic, and phonetic levels.