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Multidimensional acoustic variation in vowels across English dialects

Paper · 2022 · James Tanner, Morgan Sonderegger and Jane Stuart-Smith

Summary

Vowels are usually described by a single formant snapshot, and dialectologists tend to label them as either monophthongs or diphthongs. This paper asks how much extra information dynamic properties add when comparing vowels across many English dialects. Those properties are formant movement over time and vowel length. Using corpora pooled by the SPADE project, the authors measured five stressed vowels (FLEECE, FACE, PRICE, MOUTH, CHOICE) from 21 British and North American dialects. All measurements were taken automatically and in the same way for every corpus. They first inspected the trajectories visually. They then trained SVM classifiers to predict a token's dialect from different feature groups: formant position, trajectory shape, duration, and their combinations.

The vowels fall along a gradient from little movement (FLEECE) to strong movement (PRICE, MOUTH, CHOICE). FACE changes category depending on the region. Duration is weak as a standalone cue, but it reliably improves classification when added to formant position. Trajectory features matter most for MOUTH. The authors describe this as the largest cross-dialect study of formant dynamics for any language.

Key points

  • The dataset has 323,060 tokens (6259 word types) from 1245 speakers in 21 dialects. The data come from SPADE corpora and were processed with PolyglotDB.
  • Tokens shorter than 50 ms or longer than 500 ms were dropped.
    • Formants were sampled at 21 points, and only the central 60% (13 points) was kept.
    • Values were Lobanov-normalized within each speaker.
  • Trajectory features were built from Euclidean distances in F1/F2 space: Vector Length, Offset and Angle. Classifiers were scored by balanced accuracy against a 50% majority-label baseline.
  • The best scores came from using all features for MOUTH (70%) and PRICE (69.4%). For FACE (65.3%) and CHOICE (60.3%), formants plus duration scored best.
    • Adding duration to formants gave significant gains of 3.6–5 points for every vowel.
    • Adding trajectory to formants gave its largest gain for MOUTH (+6).
  • Scottish dialects show a monophthongal FACE and a MOUTH that fronts over its course. Elsewhere, MOUTH typically backs as it rises.

Interesting ideas

  • I1. The paper tests each feature group alone and in pairs, then checks every gain with a one-sided permutation test (1000 permutations, FDR-corrected within each vowel). This separates real gains from noise.
  • I2. A cue can be the weakest predictor on its own yet still add significant value next to stronger cues, as duration does here. This argues against discarding features based on solo performance.
  • I3. The authors treat the textbook two-way monophthong/diphthong split as something to verify, and the data show a gradient whose category boundary shifts by dialect.
  • I4. Normalizing measurements within each speaker before comparing across groups removes individual scale differences, so that group-level differences can stand out.
  • I5. The study's scale came from running one consistent automatic pipeline over many heterogeneous corpora, while openly admitting that this much output cannot be checked by hand.

For Mova

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

  • I1 (learn): When a new signal is added to a parser or repair rule, report the gain over a no-signal baseline together with a permutation-based p-value, so that small improvements are not trusted blindly.
  • I2 (tree): Before pruning a feature from the perceptron parser or gparse, measure how much accuracy drops when it is removed from the full model, not how well it predicts alone.
  • I3 (tree): When converting between UD dialects or treebank conventions, measure how consistently each disputed label is actually used per source instead of assuming the categories map one-to-one.
  • I4 (learn): Calibrate each witness parser's confidence scores against its own distribution before they vote on silver data, so that a systematically overconfident witness does not dominate.
  • I5 (tree): Harmonize all treebanks through a single shared conversion and measurement path, and record its known blind spots instead of assuming the output is clean.

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

🎓McGill UniversityMontreal · Canada87 works · 11,027 citations🎓University of GlasgowGlasgow · United Kingdom11 works · 635 citations

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksPsychology · 1,552 worksExperimental and Cognitive Psychology · 870 worksMedicine · 241 worksPhysiology · 29 works

🧭 Topics 2Research subjects and languages the work is about.English · 2,263 worksSpeech & spoken language · 480 works

Cite

Cite as James Tanner, Morgan Sonderegger and Jane Stuart-Smith. 2022. Multidimensional acoustic variation in vowels across English dialects. Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2022.sigmorphon-1.8

@inproceedings{tanner2022multidimensional,
  title = {{Multidimensional acoustic variation in vowels across English dialects}},
  author = {James Tanner, Morgan Sonderegger and Jane Stuart-Smith},
  booktitle = {Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology},
  year = {2022},
  doi = {10.18653/v1/2022.sigmorphon-1.8},
  url = {https://doi.org/10.18653/v1/2022.sigmorphon-1.8}
}

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