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Associating the origin and spread of sound change using agent-based modelling applied to /s/-retraction in English

Paper · 2019 · Mary Stevens, Jonathan Harrington and Florian Schiel

Summary

The paper asks how a stable, directional phonetic bias can turn into a real sound change once speakers talk to each other. The case study is /s/-retraction in /str/ clusters (e.g. string) in Australian English, where the sibilant drifts toward /ʃ/ but has not yet changed category. The authors extend the agent-based model (ABM) of Harrington & Schiel (2017), which implements the interactive-phonetic (IP) model. Each agent stands for one real speaker and holds probabilistic links between words, sub-phonemic classes and stored acoustic signals. A listener updates its memory only when an incoming token is accepted. The key change from the earlier model is that categories can now split and merge within each agent, so a phoneme-level shift is possible at all. After the simulated interaction, /str/ sibilants were less often grouped with prevocalic /s/ and sat acoustically nearer to /ʃ/. The authors read this as support for a view of sound change as a chance alignment of three things: the shape of phonetic distributions, how they map onto categories, and which speakers happen to interact.

Key points

  • The change modelled is /s/→/ʃ/ in /str/. Earlier work (Stevens & Harrington 2016) found lower spectral centre of gravity in /str/ for every Australian English participant. Listeners also often heard sheet when an /str/ sibilant was spliced onto /i:t/.
  • The architecture is the Harrington & Schiel (2017) ABM, an implementation of the IP model of Harrington et al. (2018). It has one agent per speaker, and all speakers come from a single, relatively homogeneous accent group.
  • The new mechanism is a split-and-merge algorithm. /s/ can split into sub-classes (s1, s2), and one of these can merge with /ʃ/. Splitting uses k-means clustering, and merging relies on an aggregated t-test over words.
  • Signals are represented by DCT coefficients over a trajectory from acoustic onset to offset, with M1 (first spectral moment) as the sibilant measure. Memory is simple: the oldest stored token is discarded.
  • The authors list several limitations:
    • The model ignores network tie strength.
    • Memory handling is too simplistic.
    • Future plans include Gaussian mixture models, likelihood-ratio tests, and representations better suited to sequence-based changes such as metathesis.

Interesting ideas

  • I1. Fixed categories that only filter input can never change, so the model lets categories split into finer sub-classes and lets a sub-class merge into a neighbour, which turns a category shift into ordinary clustering operations.
  • I2. A new sample is admitted to a class unless some rival class is the more probable fit for it, which acts as a cheap guard against categories collapsing while still allowing gradual drift.
  • I3. Merge decisions use a statistical test aggregated over many words rather than individual tokens, so one odd lexical item cannot force a structural change.
  • I4. Production draws from word-specific distributions, while perception generalises across every word sharing a class; this asymmetry keeps output conservative and input tolerant.
  • I5. The model is built so that variation exists but change is not guaranteed: tightly clustered classes stay stable, and only aligned biases produce a shift.

For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

  • I1 (learn): self-induced repair rules or silver label classes could be split with clustering when a class gets bimodal and merged when a word-level test finds no difference, instead of staying frozen.
  • I2 (learn): before adding a witness-agreed silver example to a label, check it is not nearer another label's distribution, which keeps parser retraining from drifting toward merged dependency relations.
  • I3 (tokens): decisions about merging lemma or morphology classes in crate en could require evidence summed across many lemmas, not a few noisy tokens.
  • I4 (back): generation could prefer word-specific realisations learned from the meaning graph, while the parsing side stays broadly tolerant of variant forms.
  • I5 (learn): self-play and training runs could include a negative control showing that tight, well-separated categories stay put, so any observed "learning" is not just drift.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Ludwig-Maximilians-Universität MünchenMunich · Germany113 works · 56,817 citations

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 worksSocial Sciences · 1,954 worksPsychology · 1,649 worksExperimental and Cognitive Psychology · 930 worksCultural Studies · 20 works

🧭 Topics 2Research subjects and languages the work is about.English · 2,379 worksHistorical linguistics & language change · 250 works

Cite

Cite as Mary Stevens, Jonathan Harrington and Florian Schiel. 2019. Associating the origin and spread of sound change using agent-based modelling applied to /s/-retraction in English. Glossa: a journal of general linguistics.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.5334/gjgl.620

@article{stevens2019associating,
  title = {{Associating the origin and spread of sound change using agent-based modelling applied to /s/-retraction in English}},
  author = {Mary Stevens, Jonathan Harrington and Florian Schiel},
  journal = {Glossa: a journal of general linguistics},
  year = {2019},
  doi = {10.5334/gjgl.620},
  url = {https://doi.org/10.5334/gjgl.620}
}

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  72. How to cite this article: Stevens, Mary, Jonathan Harrington and Florian Schiel. 2019. Associating the origin and spread of sound change using agent-based modelling applied to /s/-retraction in English. Glossa: a journal of general linguistics 4(1): 8. 1–30, DOI: https://doi.org/10.5334/gjgl.620 ↗
  73. Submitted: 30 January 2018 find
  74. Accepted: 15 October 2018 find
  75. Published: 14 January 2019 find
  76. Copyright: © 2019 The Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See http://creativecommons.org/licenses/by/4.0/. Glossa: a journal of general linguistics is a peer-reviewed open access journal published by Ubiquity Press. OPEN ACCESS ↗

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