ACOUSTIC ANALYSIS OF VOWEL VARIATION IN PAKISTANI ENGLISH: 13 VARIETIES 13 CITIES
Bibliographic record
Abstract
This study examines regional variation in the vowel system of Pakistani English across thirteen cities (Islamabad, Peshawar, Khuzdar etc.), representing speakers from thirteen linguistic backgrounds (Urdu, Punjabi, Sindhi, Pashto, etc.). The analysis focused on 15 vowels (12 monophthongs, 1 rhotic and 2 diphthongs), representing the core vowel inventory of Pakistani English. Speech data were automatically aligned using the Montreal Forced Aligner (MFA) (McAuliffe et al., 2017) and formant values were extracted with Praat (Boersma & Weenink, 2023). Acoustic analyses of F1, F2, and vowel duration, conducted through two-way ANOVA and Tukey HSD tests, revealed systematic regional patterns: Lahore and Islamabad speakers show fronting and vowel lengthening, Karachi speakers produce shorter and more backed vowels, and Peshawar speakers realize more open vowels with higher F1 values. Interpreted through feature geometry theory (Clements, 1985; Sagey, 1986), these patterns reflect regional re-weightings of [high], [low], [back], and [round] features, while duration differences are linked to prosodic timing and length of a vowel. The findings confirm that Pakistani English comprises distinct regional sub-varieties shaped by substrate languages and sociolinguistic factors, contributing to both the phonetic documentation of World Englishes and the pedagogical recognition of local variation in English teaching.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".