Acoustic Analysis of Front Vowels /Ɛ/ and /æ/ in Pakistani Punjabi English
Bibliographic record
Abstract
The current research is done for the verification of two different claims. According to Kachru, (2005) that Punjabi English speakers are unable to create distinction between /Ɛ/ and /æ/ front vowels but Bilal et al. (2011) has refused this claim after verifying it in the speech of Punjabi speakers of Sargodha, Pakistan. If Bilal is right than there is a big need to study this claim in broader perspective. Therefore, in the current research, 9720 utterances (of 72 native Punjabi speakers from 12 districts of Punjab, Pakistan) are recorded and analyzed in PRAAT software. Data analysis is done in two steps i.e., (i) auditory analysis is done by listening wave files and (ii) acoustic analysis is based on the measurement of first three formant values (F1, F2, F3) and vowels’ duration. The results clarify that Pakistani Punjabi English speakers have maintained difference in short and long, stressed and unstressed articulation at word initial and medial positions. But the limited number of Lahori Punjabians could not maintain this difference at word medial position only. Consequently, this research highly supports Bilal’s claim in broader perspective but we cannot totally deny Kachru’s claim. Because we have also find traces of /Ɛ/ and /æ/ merger in our data as well and the reason might be the selection of research sample.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".