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

TL;DR

This paper reviews how computational models and human perception methods study sound change over time, highlighting their differences, complementarities, and the need for integrated approaches in linguistic research.

Contribution

It provides a pioneering comparison of computational and human perception approaches to sound change, emphasizing their potential for a comprehensive understanding and calling for dataset comparisons.

Findings

Computational approaches analyze historical sound changes using etymological datasets.

Human perception models focus on ongoing sound changes through listening to recordings.

Both approaches complement each other at phonetic and acoustic levels.

Abstract

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.