Regional Distribution of the /el/-/æl/ Merger in Australian English

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Steven Coats1, Chloé Diskin-Holdaway2, and Debbie Loakes2 
1University of Oulu, Finland; 2University of Melbourne, Australia 
<a href="mailto:steven.coats@oulu.fi">steven.coats@oulu.fi</a> 
12th VarDial Workshop, أبو ظبي 
January 19th, 2025

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<div class="my-footer">Coats, Diskin-Holdaway, & Loakes&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;/el/-/æl/ Merger | فارديال أبو ظبي </div>

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## Outline

1. Introduction and Background: Prelateral vowel merger

2. Data: CoANZSE

3. Methods: Vowel extraction, Bhattacharyya difference, spatial autocorrelation

4. Results

5. Caveats, summary and outlook

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### Introduction and Background

Traditional view: Regional variation is limited. "Australia is, generally speaking, linguistically unified" (Mitchell & Delbridge 1965: 13)

AusE has “begun to exhibit more widespread social and regional variation than has previously been acknowledged” (Cox and Fletcher, 2017, p. 20)

**This study: Investigation of regional variation of prelateral merger of /e/ and [æ] in a large dataset**

- Prelateral merger of /e/ and [æ] (e.g., ***celery = salary***, ***Ellen = Alan***)

- Investigated in small-scale studies in Southern Victoria/Melbourne (Diskin et al. 2017; Diskin-Holdaway et al., 2024; Loakes et al., 2017, 2024)

- **What about in large datasets and in other locations?**

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### Methods: Vowel extraction

- Data from CoANZSE Audio (https://coanzse.org) (Coats 2024a,b)
- ASR transcripts and audio from 38,786 videos uploaded to YouTube channels of Australian councils in 404 locations
- Alignment with the Montreal Forced Aligner (McAuliffe et al. 2017)
- F1 and F2 formant values for /e/ and /æ/ extracted at vowel midpoints using Parselmouth-Praat (Jadoul et al., 2018), a Python port of Praat (Boersma & Weenink 2024)
- Vowels were extracted in two contexts: prelateral (/æl/ and /el/, e.g. *value*, *well*) and non-prelateral (/æC/ and /eC/, e.g. *fact*, *next*)
- Values from CMU pronunciation dictionary (Weide et al. 1998)
- After filtering (only stressed syllables, only locations with at least 20 tokens in all contexts): 4,297,259 vowel tokens from 252 locations
- F1 and F2 formants, values normalized with Nearey's method

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### Methods: Bhattacharyya Difference, Spatial Autocorrelation

- Many recent studies use **Pillai's trace** to quantify vowel overlap.
- Pillai's trace is is from MANOVA, used for quantifying additional covariates and generating p-values
- We use the ***Bhattacharyya Distance*** (Bhattacharyya 1943) `$D_B$`, defined as:

`$$D_B = -\log{\int \sqrt{P(x) \cdot Q(x)} \, dx}$$`

for two multivariate probability distributions `$P$` and `$Q$` (i.e., `$f_1, f_2$` for /e/ and /æ/)

- For each location, we calculate the `$D_B$` between /e/ and /æ/, based on all measurements at that location, for 
  - 1) Non-prelateral contexts (`$vC$`), and 
  - 2) Prelateral contexts (`$vL$`)

We then calculate the difference between the two contexts at each location:

`$$\text{Diff}_B = D_{\text{vC}} - D_{\text{vL}}$$`

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### Methods: Spatial autocorrelation

- Moran's *I* and Getis-Ord *G**i from `$\text{Diff}_B$`
 - Spatial weights matrix `$W$` based on distance band `$w_{ij}=1/d_{ij}$`
 - Minimum distance: all locations must have at least one neighbor

Moran's *I* (Moran 1950): Takes into account attribute values at all locations in a dataset and summarizes the overall extent of spatial correlation

- 1: perfect clustering, 0: random distribution, -1: pefect dispersion

Getis-Ord *G**i (Getis & Ord, 1992; Ord & Getis, 1995): Identifies spatial clusters by evaluating the values at each location in the dataset in comparison with neighboring locations, in relation to the global dataset

- Positive: location is ~> global mean; 0: location ~= global mean; negative: location ~< global mean

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### Results: Regional distribution

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### Results: Regional distribution, *G**i values

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### Caveats, summary, outlook

- ASR and formant extraction methods can introduce errors 
- No demographic data, but some can be semi-automatically annotated (cf. Bredin 2023; Plaquet & Bredin 2023; Ferreira 2024)
- Pipeline approaches can be used to automatically process large data volumes and extract formants for millions of vowels
- Merger confirmed as Southern Victoria/Melbourne phenomenon 
- Need to look more closely at Western Australia

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#### References

Bhattacharyya, A. (1943). On a measure of divergence between two statistical populations defined by their probability distribution. *Bulletin of the Calcutta Mathematical Society* 35, 99–110.

Boersma, P. and Weenink, D. (2024). [Praat: Doing phonetics by computer](https://www.praat.org/).

Bredin, H. (2023). [pyannote.audio 2.1 speaker diarization pipeline: Principle, benchmark, and recipe](https://www.isca-archive.org/interspeech_2023/bredin23_interspeech.html).In *INTERSPEECH 2023*, 1983-1987.

Coats, S. (2024a). Building a searchable online corpus of Australian and New Zealand aligned speech. *Australian Journal of Linguistics*.

Coats, S. (2024b). [CoANZSE Audio: Creation of an online corpus for linguistic and phonetic snalysis of Australian and New Zealand Englishes](https://aclanthology.org/2024.lrec-main.302). In *Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*, 3407-3412.

Cox, F. and Fletcher, J. (2017). *Australian English pronunciation and transcription*, 2 edition. Cambridge University Press.

Diskin, C., Loakes, D., Billington, R., Stoakes, H., Gonzalez, S., and Kirkham, S. (2019). The /el-/æl/ merger in Australian English: Acoustic and articulatory insights. In *Proceedings of the 19th International Congress of Phonetic Sciences, Melbourne, Australia 2019*, pp. 1764–1768.

Diskin-Holdaway, C., Loakes, D., and Clothier, J. (2024). Variability in cross-language and cross-dialect perception. How Irish and Chinese migrants process Australian English vowels. *Phonetica*, 81(1):1–41.

Getis, A. and Ord, J. K. (1992). [The Analysis of Spatial Association by Use of Distance Statistics](https://doi.org/10.1111/j.1538-4632.1992.tb00261.x). *Geographical Analysis* 24, 189-206.
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