An Acoustic Investigation of Pakistani and American English Vowels
Bibliographic record
Abstract
Acoustic analysis tests the hypothesis that the physical properties of Pakistani English (PaKE) vowels differ in terms of acoustic measurements of Native American English speakers. The present paper aims to document the physical behavior of English vowels produced by PaKE learners. The major goal of this paper is to measure the production of sound frequencies coupled with vowel duration. The primary aim of this paper is to explore the different frequencies and duration of the vowels involved in articulation of PaKE. English vowels selected for this purpose are: /æ/, /ɛ/, /ɪ/, /ɒ/ and /ə/. Total ten samplings were obtained from the department of computer science at Sindh Madressatul Islam University, Karachi. The study was based on the analysis of 500 (10×5×10=500) voice samples. Five vowel minimal pairs were selected and written in a carrier phrase [I say CVC now]. Ten speakers (5 male & five female) recorded their 500 voice samples using Praat speech processing tool and a high-quality microphone on laptop in a computer laboratory with no background sound. Three parameters were considered for the analysis of PaKE vowels i.e., duration of five vowels, fundamental frequency (F1 and F2). It was hypothesized that the properties of PaKE vowels are different from that of English native speakers. The hypothesis was accepted since the acoustic measurements of PaKE and English Native American speakers’ physical properties of sounds were discovered different.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".