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Can listeners differentiate varieties using prosodic information? Lect identification in a persona-matching task

Paper · 2024 · Nicole Holliday, Rachel Steindel Burdin and Paul Reed

Summary

The study asks whether listeners can tell regional and ethnic varieties of US English apart from prosody alone, or whether they depend mainly on segmental cues such as vowels. Earlier identification work mostly examined vowels and asked abstract written questions about origin. Here, the authors instead had listeners assign short speech clips to described personae, with each persona representing one of five lects: the Midwestern, Southern, Appalachian, Jewish and African American varieties of English. The clips contained H* and L+H* pitch accents. Each was presented both unfiltered and low-pass filtered, so that intonation survived while segmental detail was removed. Listener groups were Appalachian, African American, Jewish and others. With unfiltered speech, matching was at or above chance. Filtered speech still carried some information about the original lect, but accuracy fell. Errors were systematic: AAE and JE were confused with each other, as were Appalachian and Southern voices. The authors conclude that segmental cues matter more than prosody for lect identification, and that confusion patterns show which varieties share history or features.

Key points

  • Five lects were represented by personae with biographical descriptions. The tested contrasts centred on AAE, JE and Appalachian English, three varieties that production studies had already reported as prosodically distinct.
  • Stimuli were phrases containing H* or L+H* pitch accents, each in a low-pass filtered and an unfiltered version. Four listener groups took part: Appalachian, African American, Jewish and none of these.
  • The analysis was a regression over response-by-original-lect choices. It included diagonal terms (Diag(response, original)) for correct matches, plus interactions with listener Group and filtering.
    • Matching was clearly easier for some lects than others; for example, the Midwestern diagonal estimate was large.
    • Listener group shifted which responses people chose.
  • The main confusion pairs were AAE↔JE and Appalachian↔Southern. Filtering lowered accuracy but did not fully erase sensitivity to the source lect.
  • Data and R code are on OSF (osf.io/v49bk). The paper motivates the topic with the YouTube "Accent Tag" trend of 2010–2015, which produced over 40,000 videos.

Interesting ideas

  • I1. Removing one information channel (low-pass filtering strips segments but keeps pitch) shows how much a judgement depends on that channel alone; this is a clean ablation design.
  • I2. Asking people to pick among concrete described personae, rather than abstract labels like "region", makes the task closer to real-life judgement and exposes richer associations.
  • I3. Reporting the full confusion structure, not only accuracy, shows which categories share underlying features, such as common history or similar intonation.
  • I4. Modelling the choice table with separate diagonal (correct-match) terms keeps a general preference for some response option apart from real recognition ability.
  • I5. Including listener-group interactions shows that familiarity with a community changes how its speech is perceived, so the evaluator's background is part of the measurement.

For Mova

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

  • I1 (learn): build negative-control test sets that mask word identities while keeping the UD tree shape, then check whether frame or answer components still succeed when they should not.
  • I2 (answer): pose fable and tale QA as matching a quoted line to one of several described characters, which tests speaker attribution more naturally than a bare name question.
  • I3 (tree): when converting between UD dialects or comparing treebanks, inspect relation-by-relation confusion matrices to find label pairs that conventions blur, and write conversion rules for exactly those pairs.
  • I4 (learn): score witness parsers so that a parser's bias toward frequent labels is not mistaken for agreement, which makes silver-data voting more trustworthy.
  • I5 (learn): when grading with several LLM teacher tiers, model each teacher's systematic leanings as a factor instead of treating all votes as identical.

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

🎓Pomona CollegeClaremont · United States4 works · 525 citations🎓University of AlabamaTuscaloosa · United States6 works · 403 citations🎓University of New HampshireDurham · United States2 works

 Fields, topics and concepts

📚 Shelves 4The 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 works

🧭 Topics 1Research subjects and languages the work is about.Speech & spoken language · 480 works

Cite

Cite as Nicole Holliday, Rachel Steindel Burdin and Paul Reed. 2024. Can listeners differentiate varieties using prosodic information? Lect identification in a persona-matching task. 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.10124

@article{holliday2024can,
  title = {{Can listeners differentiate varieties using prosodic information? Lect identification in a persona-matching task}},
  author = {Nicole Holliday, Rachel Steindel Burdin and Paul Reed},
  journal = {Glossa: a journal of general linguistics},
  year = {2024},
  doi = {10.16995/glossa.10124},
  url = {https://doi.org/10.16995/glossa.10124}
}

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