Regional Variation in Australian English Monophthongs

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Steven Coats 
University of Oulu, Finland 
<a href="mailto:steven.coats@oulu.fi">steven.coats@oulu.fi</a> 
ALS Conference, Griffith University 
December 4th, 2025

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<div class="my-footer">Steven Coats&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;AusE Monophthongs | ALS25</div>

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## Outline

1. Background

2. Methods: GAMM with a spatial component, spatial autocorrelation

3. Data: CoANZSE

4. Preliminary results

5. Outlook and summary

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### Background

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

- "Short a" (/æ/) in words like *dance*: Hobart > Melbourne, Sydney, and Brisbane; Adelaide < Melbourne, Sydney, and Brisbane (Horvath and Horvath 2001; Bradley 1991; 2008)

- Lowering of prelateral /e/: More in Melbourne/VIC (Bradley, 2008; Coats et al. 2025; Cox and Palethorpe 2004; Diskin et al. 2019; Loakes et al. 2011, 2014, 2017, 2024; Schmidt et al. 2021)

- No difference in ~3500 monophthongs in hVd words from AusTalk speakers from Sydney, Melbourne, Perth, and Adelaide (Cox and Palethorpe 2019)

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### Methods

1. Mean F1 and F2 values extracted from naturalistic speech at a large number of AUS locations

2. GAMMs with spatial smooths (cf. Wirtz et al. 2025)
 - Fixed effects for *state*, *previous consonant class*, and *following consonant class*; a 2D spatial smooth over longitude and latitude; and a random intercept for council
 - Incorporate latitude and longitude coordinates as a smoothing term
 - f1_z ~ state + prev_class + next_class + s(longitude, latitude) + 
 s(council, bs = "re")

3. Spatial autocorrelation (cf. Grieve 2017)
 - Quantify the extent and scale of spatial patterning

- Do F1 and F2 vary by geographic location?
- What is the role of urban centers? 
- Are spatial patterns consistent with proposed ongoing chain shifts?

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### Data: CoANZSE Audio (Coats 2022, 2024)

- ASR transcripts and audio from YouTube channels of regional and local councils

- Many recordings are meetings: advantages in terms of representativeness and comparability

- Speaker place of residence (cf. videos collected based on place-name search alone)

- Topical contents and communicative contexts comparable

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### Example video

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### WebVTT file

![:scale 75%](data:image/png;base64,#./gc_webvtt.png)

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### Pipeline: Spatial analysis

Tobler's first law: "everything is related to everything else, but near things are more related than distant things" (Tobler 1970)

- Moran's *I* and Getis-Ord *G**i based on mean F1 and F2 values at each location
 - Spatial weights matrix `\(W\)` based on distance band `\(w_{ij}=1/d_{ij}\)`
 - Minimum distance: all locations must have at least one neighbor
 - For each vowel and formant, only locations with at least 200 tokens considered
 - Spatial analysis conducted for AUS and NZ separately
 - Calculated with esda and pysal Python packages

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 of similar values, 0: random spatial distribution of values, -1: pefectly even dispersion of values

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 for the location and its neighbors > global mean; 0: value = global mean; negative: value < global mean

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### CoANZSE (Coats 2024a, 2024b) alignment

- Australian data: 38,786 videos from 408 councils (≈ 13,885 hours)

- All audio, cut into 20-second segments, retrieved from <https://coanzse.org>

- Forced aligment with **Montreal Forced Aligner** (McAuliffe et al. 2017) v3.0.0, with its **UK English** acoustic model, G2P model, pronunciation dictionary, and the **adapt** functionality (council-specific acoustic adaptation)

- Output: Praat TextGrid files with tiers for orthographic transcript and phones

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### Vowel and formant extraction

- Formants were measured at the durational midpoint of each vowel segment using *Parselmouth-Praat* (Jadoul et al. 2018), a Python port of Praat functions (Boersma & Weenink 2024)

- Praat settings (default):  
  - 5 formants, max formant 5,500 Hz  
  - time step automatically determined from vowel duration  
  - window length 0.025 s  
  - pre-emphasis above 50 Hz  
  
- Extracted vowel duration, **F1**, **F2**, and bandwidths

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### Gender inference

- YouTube ASR transcripts lack diarization and metadata about speaker gender, so segments needed post-processing to control for gender imbalance across council locations

- Speaker sex/gender was inferred using [alefiury/wav2vec2-large-xlsr-53-gender-recognition-librispeech](https://huggingface.co/alefiury/wav2vec2-large-xlsr-53-gender-recognition-librispeech), a wav2vec2-based model reported to reach 99.93% accuracy

- Gender inference was run on the first 5 seconds of 3.68 million audio files using 4 AMD MI250X GPUs

- Extracting vowel tokens from the TextGrids for this audio = 37.3 million vowel tokens labeled “male” or “female”

- Manual evaluation of 100 randomly sampled segments showed 97% observed accuracy, with 97 model labels matching human annotations

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### Filtering

- /iː, ɪ, e, ʉː, æ, ɐ, oː, ɔ, ʊ, ɜː/ were retained (no. tokens = 21,458,728)

- Remove unstressed vowels, vowels followed by nasals or liquids, and short stop words

- Stress from Carnegie-Mellon Pronunciation Dictionary (Weide et al. 1998), stopwords from NLTK (Bird et al. 2009)

- For some tokens, unrealistic formant values, likely the result of formant tracking errors

- Outliers were removed with a Mahalanobis distance filter for F1 and F2 on the basis of the critical value of the 95% quantile of the `\(\chi ^{2}\)` distribution, following widespread practice (Labov et al. 2013; Mielke et al. 2019; Renwick and Stanley 2020)

---

### Overview of vowel tokens

Study data: 7.2m vowel tokens

![](data:image/png;base64,#Coats_Fig1.png)

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### Vowel tokens by state/territory, sex, and vowel (N = 7,208,190)

| State | Sex | iː | ɪ | e | æ | ɐ | ɔ | oː | ʊ | ʉː | ɜː |
|-------|-----|-------|--------|--------|--------|--------|--------|--------|--------|--------|--------|
| ACT | f | 2,390 | 7,578 | 4,004 | 3,581 | 1,098 | 273 | 303 | 349 | 1,143 | 1,285 |
| ACT | m | 2,421 | 8,040 | 4,144 | 4,032 | 1,233 | 334 | 219 | 371 | 1,197 | 1,233 |
| NSW | f | 83,695 | 257,451 | 134,456 | 109,927 | 34,572 | 11,132 | 10,970 | 17,251 | 38,637 | 30,798 |
| NSW | m | 108,677 | 385,683 | 192,563 | 160,316 | 59,865 | 16,822 | 15,902 | 25,747 | 64,913 | 47,732 |
| NT | f | 492 | 1,440 | 785 | 661 | 211 | 48 | 85 | 85 | 200 | 193 |
| NT | m | 481 | 1,251 | 740 | 679 | 193 | 64 | 89 | 114 | 231 | 198 |
| QLD | f | 55,876 | 162,321 | 85,875 | 73,009 | 24,453 | 7,722 | 6,233 | 8,498 | 27,015 | 22,681 |
| QLD | m | 92,840 | 279,219 | 146,002 | 133,499 | 54,420 | 14,689 | 10,326 | 14,824 | 52,521 | 40,802 |
| SA | f | 42,270 | 119,163 | 68,846 | 56,959 | 17,104 | 5,288 | 5,031 | 8,759 | 19,779 | 15,903 |
| SA | m | 58,634 | 182,700 | 94,909 | 86,676 | 27,588 | 8,153 | 7,229 | 14,125 | 31,230 | 25,024 |
| TAS | f | 12,715 | 39,984 | 21,112 | 16,745 | 6,139 | 1,642 | 1,454 | 2,203 | 6,399 | 5,332 |
| TAS | m | 18,488 | 70,653 | 36,424 | 30,720 | 12,197 | 2,949 | 2,427 | 4,147 | 11,335 | 8,834 |
| VIC | f | 132,280 | 395,838 | 198,137 | 156,966 | 53,022 | 17,662 | 15,002 | 19,840 | 61,939 | 52,368 |
| VIC | m | 131,883 | 433,326 | 213,884 | 180,280 | 69,976 | 20,860 | 15,725 | 24,089 | 71,745 | 58,958 |
| WA | f | 32,200 | 87,996 | 49,892 | 40,366 | 11,432 | 4,300 | 3,491 | 5,197 | 15,397 | 13,140 |
| WA | m | 27,008 | 80,400 | 42,980 | 38,851 | 12,807 | 3,611 | 2,816 | 4,110 | 14,569 | 13,176 |
| **Total** |  | 802,350 | 2,513,043 | 1,294,753 | 1,093,267 | 386,310 | 115,549 | 97,302 | 149,709 | 418,250 | 337,657 |

]

---

### Normalization of formant values

- To account for average vocal-tract size differences between males and females (Adank et al. 2004; Barreda & Nearey 2017; Fabricius et al. 2009; Flynn 2011; Thomas & Kendall 2007)

- For the GAMM analysis, a formant-intrinsic, vowel-extrinsic normalization was applied: 
 - For each **gender × vowel**, formant values were scaled to **z-scores**
 
- Method is appropriate for studies with large numbers of speakers (Thomas & Kendall 2007)

---

### Results: Formant values

![](data:image/png;base64,#Coats_Fig2.png)

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### Results: Formant values

![](data:image/png;base64,#Coats_Fig3.png)

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### Results: GAMM

| Vowel | # Tokens | F1 (notable state effects) | F1 p-value | F2 (notable state effects) | F2 p-value |
|-------|----------|-----------------------------|------------|-----------------------------|------------|
| /iː/  | 801,236  | QLD: −0.33 \*, TAS: −0.38 (\*) | \*\*\* | NSW: −0.23 (\*), TAS: −0.42 (\*) | \*\*\* |
| /ɪ/   | 2,512,056 | NT: +0.82 \*\*\*, SA: +0.33 \*, WA: +0.84 \*\*\* | \*\* | SA: −0.26 \* |  |
| /e/   | 1,293,416 | TAS: −0.53 \* | \*\*\* | NT: −0.56 \*\*, SA: −0.36 \*\*, TAS: −0.21 (\*), WA: −0.77 \*\* | (\*) |
| /æ/   | 1,092,101 | TAS: −0.51 \* | \*\*\* | NT: −0.42 \*, WA: −0.35 (\*) | \*\* |
| /ɐ/   | 384,757  | NT: +0.81 (\*), TAS: −0.50 \* | \* |  | \* |
| /ɔ/   | 113,494  | TAS: −0.46 (\*) | \* |  |  |
| /oː/  | 95,317   |  | \* | QLD: −0.28 (\*) |  |
| /ʊ/   | 147,785  | NT: +1.06 (\*) |  |  |  |
| /ʉː/  | 416,740  |  | \* | TAS: −0.34 \* |  |
| /ɜː/  | 335,894  | NT: +1.16 \*, TAS: −0.52 \* | \* |  | (\*) |

(\*): p ≤ 0.1; \*: p ≤ 0.05; \*\*: p ≤ 0.01; \*\*\*: p ≤ 0.001

TAS: Simply more "short-a"? Ordinary least-squares models were created for both genders with only /æ/ tokens from the highly frequent item *that’s* (112,029 occurrences)

- Here as well, /æ/ is higher for TAS speakers

]

---

### Results: GAMM

![:scale 65%](data:image/png;base64,#ae_f1_z_heatmap_v2.png)

---

### Results: Spatial Autocorrelation (Getis & Ord 1992; Moran 1950; Ord & Getis 1995)

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### Preliminary findings

- Geographic variation: NT, SA, WA show lower /ɪ/ and more back /e/; TAS shows higher /e, æ, ɐ/

- Spatial autocorrelation: Moran’s I is weak overall, but Gi* reveals fine-scale pockets of variation that state boundaries don't capture; magnitude of local spatial autocorrelation values is not high

- Continuous spatial gradients: both GAMM and Gi* show geographic trends for most vowels, especially front vowels, radiating outward from major cities (Sydney, Melbourne, Brisbane, Adelaide, Perth) (cf. Cox and Penney 2024; Travis 2023)

- Possibly influenced by L2 English of immigrants (Cox et al. 2024; Grama et al. 2021; Travis et al. 2023)

- Chain shift evidence: Patterns in /e, æ, ɐ, ɪ/ match proposals for recent front-vowel chain shifts: "anticlockwise rotation of vowels within the F1/F2 space" (Cox et al. 2024, p. 16; Cox & Palethorpe 2008) evident

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### Outlook/caveats

- Data: ASR accuracy, formant extraction, MFA pronunciation dictionary/AusE phone mismatch
- GAMM: No speaker info, so no speaker random effects. No lexical item random effects (>10k types)

Outlook:

- Carefully curated subset of data (high-quality council recordings, subset of word list) may give more robust results

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### Summary

- Pipeline approaches can be used to automatically process large data volumes and extract formants for millions of vowels

- Incipient regional variation attested for Australia

- Australian cities may be leading the changes

- Still much to be done!

#### Thank you for your attention!

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