---
title: "Regional Distribution of the /el/-/æl/ Merger in Australian English"
description: "Victoria and Melbourne are known for merging /e/ and /æ/ before /l/, so that pairs like celery/salary… 54 references, cited by 1 paper here."
authors: ["Steven Coats", "Chloé Diskin-Holdaway", "Debbie Loakes"]
year: 2025
venue: "Proceedings of the 12th Workshop on NLP for Similar Languages, Varieties and Dialects"
source: "https://aclanthology.org/2025.vardial-1.11/"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/coats-2025-regional-distribution-el-l-merger/"
---
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# Regional Distribution of the /el/-/æl/ Merger in Australian English

**Authors:** [Steven Coats](https://md.research.elamur.ai/free/en/authors/steven-coats/), Chloé Diskin-Holdaway and Debbie Loakes · **Year:** 2025 · **Venue:** [Proceedings of the 12th Workshop on NLP for Similar Languages, Varieties and Dialects](https://md.research.elamur.ai/free/en/venues/vardial/) ([VarDial 2025](https://md.research.elamur.ai/free/en/venues/vardial/2025/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2025.vardial-1.11/) · [PDF](https://aclanthology.org/2025.vardial-1.11.pdf)

**Citations:** [1](https://md.research.elamur.ai/free/en/papers/coats-2025-regional-distribution-el-l-merger/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Victoria and Melbourne are known for merging /e/ and /æ/ before /l/, so that pairs like celery/salary sound the same. Most earlier evidence came from small word-list recordings in a few towns, so it was unclear whether the merger also occurs elsewhere in Australia. The authors tested this nationwide using naturalistic speech from CoANZSE Audio, a corpus of YouTube videos from local councils. Their pipeline aligned ASR transcripts to audio with the Montreal Forced Aligner, measured F1/F2 at vowel midpoints with Parselmouth-Praat, and removed unstressed vowels and stopwords. For each location, they measured how much the two vowels overlap using Bhattacharyya distance. The prelateral overlap was then contrasted with overlap in other consonant contexts, and the resulting scores were mapped with spatial autocorrelation. The merger is strongest in Victoria, especially the south, and weakest in Sydney and New South Wales. There are also early signs of it in other regions, such as Western Australia. The study shows that large, noisy, unlabelled speech collections can support regional phonetics, although speaker-level normalization remains an open problem.

## Key points
- The source data was 38,786 council videos covering 404 locations. Alignment produced 9,264,705 vowel tokens, and after filtering 4,297,259 tokens from 252 locations remained.
- A location was kept only if it had at least 20 tokens in each of four cells: /æl/, /æC/, /el/ and /eC/. Stress information came from the CMU dictionary, and stopwords were removed using an NLTK list.
- Average Bhattacharyya distance was 0.173 before /l/ and 0.431 in other contexts (t = −31.037, p \< 0.001).
- The transcripts have no diarization, so per-speaker normalization was impossible. The authors used a z-scaled Nearey transform instead.
- The CoANZSE ASR word error rate is 0.14. Some errors probably hide merger cases, for example celery being transcribed as salary.

## Interesting ideas
- The merger score is the prelateral overlap minus the overlap in a control context, so effects that apply to all contexts at a location cancel out.
- Bhattacharyya distance is used to compare distributions instead of Pillai's trace, because it does not assume normality and is less sensitive to uneven sample sizes.
- Every location must reach a minimum count in all four context cells before it is analysed. This stops sparse regions from producing unreliable estimates.
- The authors point out that recognition errors can bias against the very phenomenon being studied, since a merged word may be written as its neighbour.
- The authors scale up with a fully automatic pipeline but plan to validate it on a manually annotated curated subset.

## For Mova

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

- **I1** (learn): When measuring a dialect or genre effect on parser accuracy, compare against a matched control construction in the same texts, which works as a built-in negative control.
- **I2** (learn): It could compare score or confidence distributions from witness parsers across corpora of very different sizes without assuming a Gaussian shape.
- **I3** (answer): Evaluation reports broken down by text type or construction could suppress any cell with too few items, so that small slices are not over-read.
- **I4** (tokens): When tagger or lemma input comes from ASR or OCR, audit near-homophone confusions separately, because they can systematically erase dialect signals instead of adding random noise.
- **I5** (tree): Treebanks produced by UD dialect conversion could be checked the same way, using a small hand-verified sample to calibrate the large automatic set.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Steven Coats, Chloé Diskin-Holdaway and Debbie Loakes. 2025. Regional Distribution of the /el/-/æl/ Merger in Australian English. Proceedings of the 12th Workshop on NLP for Similar Languages, Varieties and Dialects.

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

```bibtex
@inproceedings{coats2025regional,
  title = {{Regional Distribution of the /el/-/æl/ Merger in Australian English}},
  author = {Steven Coats, Chloé Diskin-Holdaway and Debbie Loakes},
  booktitle = {Proceedings of the 12th Workshop on NLP for Similar Languages, Varieties and Dialects},
  year = {2025},
  url = {https://aclanthology.org/2025.vardial-1.11/}
}
```

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## References

54 references, 2 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 1 November 2026*