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Towards a dynamical model of English vowels. Evidence from diphthongisation

Paper · 2024 · Patrycja Strycharczuk, Sam Kirkham, Emily Gorman et al.

Summary

English phonology usually separates diphthongs, built from two parts, from monophthongs, built from one. Acoustic and articulatory measurements, however, show that the amount of vowel-internal movement varies continuously, so the boundary is hard to state. The authors ask whether diphthongs form a coherent articulatory class. They recorded six speakers of Northern Anglo-English with electromagnetic articulometry (EMA) and simultaneous acoustics, covering every phonologically long vowel. They applied several diphthongisation measures, including functional PCA over articulator trajectories. No measure produced a clean split between diphthongs and long monophthongs. To explain this, they propose an Articulatory Phonology/Task Dynamics model, backed by computational simulation, in which every long vowel has two gestural targets. Diphthongs and long monophthongs differ only in how far apart the constriction and location settings of those two targets are. Under this view, phonological weight and duration come from having two targets, and the degree of diphthongisation is a gradient by-product of how dissimilar the targets are. The account also fits historical diphthongisation and present-day dynamic variation in British English.

Key points

  • The data are EMA and acoustic recordings from 6 Northern Anglo-English speakers, covering the full set of phonologically long vowels.
  • None of the diphthongisation metrics separated diphthongs from long monophthongs as distinct categories.
  • The proposed model gives all long vowels two targets. In diphthongs the targets differ in their gestural specifications. In long monophthongs the targets share the same specifications.
  • The authors contrast their model with existing accounts:
    • Goldsmith's autosegmental root-node distinction
    • Marin's blending-strength and coupling model for Romanian
    • TADA's current one-versus-two-target encoding, which already gives FLEECE and GOOSE two gestures
  • Methodological advice: measure every long vowel at two or more time points, or use a data-driven reduction such as fPCA or the Discrete Cosine Transform. Do not measure only the midpoint for "monophthongs". The study was published in Journal of Phonetics 107, 101,349 and funded by AHRC grant AH/S011900/1.

Interesting ideas

  • I1. When two supposed categories blur into each other, give both the same structure and let the visible difference come from how far apart the internal parameters are.
  • I2. Choose one representation by asking what property all members share (here, weight and duration from two targets), not by what makes some of them stand out.
  • I3. Instead of picking measurement points by hand from a theory, let dimensionality reduction such as fPCA find the main modes of variation directly from the data.
  • I4. The authors argue that the proposal can only be validated properly by simulating it: varying parameters while keeping the structure fixed should reproduce realistic variation.
  • I5. The model is backed by several independent kinds of evidence (articulation, phonological weight, historical change, dialect variation), not by one measurement alone.

For Mova

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

  • I1 (world): Measures and states could share one slot schema, with "gradable" versus "absolute" readings arising from parameter values rather than separate entity types.
  • I2 (tokens): Borderline classes such as participle-adjectives could get one shared morphological analysis, with the noun-like or verb-like character expressed as a feature value.
  • I3 (learn): Parser or frame error profiles could be reduced with PCA-like methods to find the real axes of failure, instead of relying on hand-picked error buckets.
  • I4 (back): Generating paraphrases from a fixed meaning graph with only parameter changes would test whether surface variation is fully explained without adding new structure.
  • I5 (learn): A repair rule could be accepted only when independent witnesses agree, for example parsers, VerbNet frames and world consistency, mirroring this convergent-evidence standard.

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

🎓University of ManchesterManchester · United Kingdom93 works · 45,138 citations🎓Lancaster UniversityLancaster · United Kingdom36 works · 1,615 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 worksPsychology · 1,649 worksExperimental and Cognitive Psychology · 930 works

🧭 Topics 2Research subjects and languages the work is about.English · 2,379 worksSpeech & spoken language · 519 works

Cite

Cite as Patrycja Strycharczuk, Sam Kirkham, Emily Gorman and Takayuki Nagamine. 2024. Towards a dynamical model of English vowels. Evidence from diphthongisation. arXiv:2409.00275 · Journal of Phonetics 107, 101,349 (2024).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1016/j.wocn.2024.101349 · arXiv 2409.00275

@misc{strycharczuk2024dynamical,
  title = {{Towards a dynamical model of English vowels. Evidence from diphthongisation}},
  author = {Patrycja Strycharczuk, Sam Kirkham, Emily Gorman and Takayuki Nagamine},
  year = {2024},
  doi = {10.1016/j.wocn.2024.101349},
  eprint = {2409.00275},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.1016/j.wocn.2024.101349}
}

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