---
title: "Exploring the anatomy of articulation rate in spontaneous English"
description: "Studies of speech rate often report differences by dialect, age and gender, but the evidence is inconsistent. 49 references."
authors: ["James Tanner", "Morgan Sonderegger", "Jane Stuart-Smith", "Tyler Kendall", "Jeff Mielke", "Robin Dodsworth", "Erik Thomas"]
year: 2024
venue: "arXiv:2408.06732"
source: "https://arxiv.org/abs/2408.06732"
doi: "10.48550/arxiv.2408.06732"
arxiv: "2408.06732"
licence: "CC BY-NC-SA 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by-nc-sa/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/tanner-2024-exploring-anatomy-articulation-rate-spontaneous/"
---
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# Exploring the anatomy of articulation rate in spontaneous English speech: relationships between utterance length effects and social factors

**Authors:** [James Tanner](https://md.research.elamur.ai/free/en/authors/james-tanner/), [Morgan Sonderegger](https://md.research.elamur.ai/free/en/authors/morgan-sonderegger/), [Jane Stuart-Smith](https://md.research.elamur.ai/free/en/authors/jane-stuart-smith/), [Tyler Kendall](https://md.research.elamur.ai/free/en/authors/tyler-kendall/), [Jeff Mielke](https://md.research.elamur.ai/free/en/authors/jeff-mielke/), Robin Dodsworth and Erik Thomas · **Year:** 2024 · **Venue:** arXiv:2408.06732 · **Licence:** [CC BY-NC-SA 4.0 (arXiv)](http://creativecommons.org/licenses/by-nc-sa/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2408.06732) · [PDF](https://arxiv.org/pdf/2408.06732) · [DOI 10.48550/arxiv.2408.06732](https://doi.org/10.48550/arxiv.2408.06732) · [arXiv 2408.06732](https://arxiv.org/abs/2408.06732) · [OpenAlex](https://openalex.org/W4402427108)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Studies of speech rate often report differences by dialect, age and gender, but the evidence is inconsistent. A strong competing factor is utterance length: longer stretches of speech tend to be faster and more even in tempo, an instance of Menzerath's Law. Earlier work looked at only a few dialects, or covered many dialects without controlling for length. The authors pooled 13 spontaneous English corpora from the UK, US and Canada, accessed through the ISCAN toolkit. They measured articulation rate, meaning syllables per second excluding pauses, using UNISYN syllabifications. A distributional Bayesian multilevel model in brms/Stan then estimated how utterance length, age and gender shape both the mean and the variance of rate, with each effect allowed to vary by corpus. Utterance length had by far the largest effect, and it was strikingly uniform across corpora and speakers. Age and gender effects remained after controlling for length but were much smaller. The authors read this as a sign that length effects come from shared articulatory and perceptual constraints. Social differences in tempo should therefore be judged against that larger, stable backdrop.

## Key points
- **Data:** 116,020 utterances from 1092 speakers (510 female), drawn from 13 corpora recorded between 1970 and 2013. Examples include Buckeye, Switchboard and DECTE.
- **Utterance definition and filtering:** utterances are stretches bounded by pauses of at least 150 ms. Those under 3 syllables were dropped, as were rates beyond ±2 SD of each speaker's own mean, since these mostly reflected forced-alignment errors.
- **Length predictor:** log utterance length was split into a speaker's average length and each utterance's deviation from that average. Both parts were fitted with cubic splines using 5 knots.
- **Effect sizes:**
  - Moving from the shortest to the longest utterances raises rate by about 2.1–2.4 syll/sec.
  - Age lowers rate by roughly 0.7–0.8 syll/sec across the observed age range.
  - Women are about 0.21–0.24 syll/sec slower than men.
- **Variation across groups:** corpora (σ≈0.07) and speakers (σ≈0.09) differ in baseline rate, with no clear evidence that speaker variation exceeds corpus variation (Pr=0.87). Corpora whose gender effect overlaps zero are mostly the smallest ones.

## Interesting ideas
- Splitting a predictor into a per-group average and a within-group deviation separates "this source is generally X" from "this item is unusually X for its source."
- Modelling the variance of an outcome alongside its mean shows whether a factor makes behaviour less stable, not only higher or lower.
- Outliers are defined relative to each source's own typical value rather than a global threshold, which catches pipeline failures without penalising naturally extreme sources.
- If an effect stays stable across many heterogeneous corpora, the authors take that as evidence of a shared mechanism rather than a local convention.
- Factors are compared on one common scale, with an explicit probability that one effect exceeds another, so small but real effects are kept in proportion.

## For Mova

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

- **I1** (learn): when analysing parser errors by sentence length, split each treebank's mean length from each sentence's relative length so genre effects are not mistaken for length effects.
- **I2** (learn): evaluation reports could model the spread of LAS across sentences or documents, not just the average, to locate conditions where the system becomes erratic.
- **I3** (learn): silver data could flag sentences whose witness-parser disagreement is far above that corpus's usual level, treating them as likely tokenization or conversion glitches.
- **I4** (tree): checking whether a parsing difficulty recurs with similar size across several treebanks and UD dialects would help separate genuine structural difficulty from annotation-convention artefacts.
- **I5** (learn): ablations could state how likely one component's gain is to exceed another's, instead of only listing point scores.

*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 University](https://md.research.elamur.ai/free/en/universities/mcgill-university/) — University, Montreal, Canada · 87 works · 11,027 citations
- [University of Oregon](https://md.research.elamur.ai/free/en/universities/university-of-oregon/) — University, Eugene, United States · 23 works · 3,272 citations
- [North Carolina State University](https://md.research.elamur.ai/free/en/universities/north-carolina-state-university/) — University, Raleigh, United States · 13 works · 406 citations
- [University of Glasgow](https://md.research.elamur.ai/free/en/universities/university-of-glasgow/) — University, Glasgow, United Kingdom · 11 works · 635 citations

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## Cite

Cite as: James Tanner, Morgan Sonderegger, Jane Stuart-Smith, Tyler Kendall, Jeff Mielke, Robin Dodsworth and Erik Thomas. 2024. Exploring the anatomy of articulation rate in spontaneous English speech: relationships between utterance length effects and social factors. arXiv:2408.06732.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{tanner2024exploring,
  title = {{Exploring the anatomy of articulation rate in spontaneous English speech: relationships between utterance length effects and social factors}},
  author = {James Tanner, Morgan Sonderegger, Jane Stuart-Smith, Tyler Kendall, Jeff Mielke, Robin Dodsworth and Erik Thomas},
  year = {2024},
  doi = {10.48550/arxiv.2408.06732},
  eprint = {2408.06732},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2408.06732}
}
```

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*Updated 1 November 2026*