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Uniformity in speech: The economy of reuse and adaptation across contexts

Paper · 2024 · Donald Derrick, Connor Mayer and Bryan Gick

Summary

The paper asks why North American English (NAE) flaps/taps vary so widely in tongue-tip motion. Two explanations compete. Under the mechanical hypothesis, flaps vary on their own wherever a different motion pattern makes articulation easier. Under the uniformity hypothesis, the variability starts in rhotic vowels, and speakers then reuse those already-learned motion patterns in flap contexts with no rhotic nearby. The way to tell them apart is a dialect that has flaps but no rhotic vowels. New Zealand English (NZE) is such a dialect.

The authors recorded electromagnetic articulography (EMA) from NZE and NAE speakers. Speakers produced /VɾVɾV/ sequences with only non-rhotic vowels at slow and fast speech rates. The authors measured tongue position at the sequence midpoint and compared dialects using statistical models. NZE speakers used one similar tip-motion pattern at both rates. Some NAE speakers instead switched between two stable patterns, one for slow speech and one for fast. Extreme flap variability therefore does not appear in every English dialect, which supports uniformity. The broader point is that reusing existing motor routines can compete with pure ease of movement.

Key points

  • Both NAE /ɹ/ and NAE flaps/taps can be produced in categorically different ways that sound nearly the same. For /ɹ/, the key acoustic cue is low F3. For flaps, the differences between strategies show up mostly in F2.
  • NAE flaps come in four types: alveolar tap, post-alveolar tap, up-flap and down-flap (from Derrick & Gick 2011).
  • NZE has flaps only between vowels and variable prevocalic /ɹ/, but no rhotic vowels. This makes it a natural test case.
  • The authors find that NZE speakers do not switch flap strategies with speech rate, while some NAE speakers do. The result supports the uniformity hypothesis over the mechanical one.
  • The EMA data, statistical tests (including the best-fit model behind Table 4 and Figure 4a) and plotting code are on OSF. The work was funded by a Marsden fast-start grant (12-UOC-081).

Interesting ideas

  • I1. The study tests a causal claim by finding a population that has the outcome site (flaps) but lacks the suspected trigger (rhotic vowels), which serves as a natural negative control.
  • I2. Under uniformity, a speaker keeps reusing an entrenched solution in new contexts unless the cost becomes too high, so variation spreads only where an alternative already exists in the repertoire.
  • I3. The design states two hypotheses whose predictions diverge on one measurable case and then measures that case directly, rather than collecting data broadly.
  • I4. Varying one condition (speech rate) while measuring at a fixed anchor point (the midpoint) shows whether a system has a single stable mode or flips between discrete modes.
  • I5. Several distinct production strategies can serve the same perceptual target, and the choice among them depends on context cost.

For Mova

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

  • I1 (learn): Check a self-induced repair rule on texts that lack the construction we believe triggers it; if the rule still fires there, the claimed cause is wrong.
  • I2 (tree): When the parser must choose between near-equal analyses, it could prefer one it has already used in the same document or for the same verb, and switch only when the score gap is large.
  • I3 (learn): Before adding a component, write down which test items should behave differently under the competing explanations and evaluate on exactly those items.
  • I4 (learn): Run the parser on the same clause embedded in shorter and longer sentences and check whether its analysis jumps between stable alternatives as length grows.
  • I5 (back): Generation could keep a set of equivalent surface realizations for one meaning and pick among them by local fluency, while staying consistent with choices already made in the same text.

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 British ColumbiaVancouver · Canada104 works · 37,403 citations🎓University of California, IrvineIrvine · United States43 works · 36,565 citations🎓University of CanterburyChristchurch · New Zealand5 works · 274 citations

Also on the byline: Haskins Laboratories.

 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 worksPsychology · 1,649 worksExperimental and Cognitive Psychology · 930 worksMedicine · 275 worksPhysiology · 35 works

Cite

Cite as Donald Derrick, Connor Mayer and Bryan Gick. 2024. Uniformity in speech: The economy of reuse and adaptation across contexts. Glossa: a journal of general linguistics.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.16995/glossa.11681

@article{derrick2024uniformity,
  title = {{Uniformity in speech: The economy of reuse and adaptation across contexts}},
  author = {Donald Derrick, Connor Mayer and Bryan Gick},
  journal = {Glossa: a journal of general linguistics},
  year = {2024},
  doi = {10.16995/glossa.11681},
  url = {https://doi.org/10.16995/glossa.11681}
}

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