Acoustic classification of coronal stops of Eastern Punjabi
Bibliographic record
Abstract
Punjabi is an Indo-Aryan language which contrasts a rich set of coronal stops at dental and retroflex places of articulation across three laryngeal configurations. Moreover, all these stops occur contrastively in various positions (word-initially, -medially, and -finally). The goal of this study is to investigate how various coronal place and laryngeal contrasts are distinguished acoustically both within and across word positions. A number of temporal and spectral correlates were examined in data from 13 speakers of Eastern Punjabi: Voice Onset Time, release and closure durations, fundamental frequency, F1-F3 formants, spectral center of gravity and standard deviation, H1*-H2*, and cepstral peak prominence. The findings indicated that higher formants and spectral measures were most important for the classification of place contrasts across word positions, whereas laryngeal contrasts were reliably distinguished by durational and voice quality measures. Word-medially and -finally, F2 and F3 of the preceding vowels played a key role in distinguishing the dental and retroflex stops, while spectral noise measures were more important word-initially. The findings of this study contribute to a better understanding of factors involved in the maintenance of typologically rare and phonetically complex sets of place and laryngeal contrasts in the coronal stops of Indo-Aryan languages.
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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".