Acoustics of guttural fricatives in Arabic, Armenian, and Kurdish: A case in remote data collection
Bibliographic record
Abstract
Gutturals – uvulars, pharyngeals, and laryngeals – are relatively phonetically under-studied, with previous acoustic investigations being limited to a handful of languages (and mainly Arabic). The goal of this paper is twofold: (i) to provide an acoustic documentation of guttural fricatives /χ,ʁ,(ħ,ʕ),h/ in three under-documented languages/dialects – Emirati Arabic, Iraqi Central Kurdish, and Lebanese Western Armenian, and (ii) through this to test the reliability of remote data collection for the analysis of fricatives. Fifty-nine participants residing in United Arab Emirates, Iraq, and Lebanon (18-21 per language) completed an online audio-recording experiment. Word-initial, -medial, and -final fricatives in real words, embedded in carrier phrases, were measured for four spectral moments, relative intensity, and duration. The results showed consistent place and voicing differences in all three languages. Specifically, center of gravity and standard deviation of fricative noise were higher for uvulars and lower for pharyngeals and /h/. Voicing was consistently distinguished by duration, among other variables. Some positional and gender differences were also observed. Overall, the results obtained for fricatives in three languages are remarkably similar to those previously reported for Arabic and other languages, providing evidence for shared acoustic properties of gutturals, as well as confirming the validity of the remote audio recording method.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".