The Influence of Guttural Consonants /χ/, /ħ/, and /h/ on Vowel /a/ in Saudi Arabic
Bibliographic record
Abstract
This paper presents a comparative study which investigates the influence of Saudi Arabic guttural consonants /χ/, /ħ/ and /h/ on the vowel /a/ when they are adjacent and in the same syllable. Cohn (2007, 2009), Flemming (2001), and Keating (1996) discuss a unified model in which phonology and phonetics are treated as two distinct elements of one domain where each element has an effect on the other to some degree. McCarthy (1991, 1994), Rose (1996), Zawaydeh (1999, 2004), and BinMuqbil (2006) presented phonological studies on gutturals, as well as discussions on gutturals as a natural class, which uphold the phonological aspect of Cohn’s (2009) unified model. The aim of this study is to address the phonetic aspect of Cohn’s (2009) unified model by analyzing the phonetic effects of guttural-vowel coarticulation. An acoustic analysis method was used as a framework for this investigation to extract first formant frequency (F1) and second formant frequency (F2) to measure the influence in the coarticulation. For the purpose of this study, seven native Saudi Arabic speakers were recorded pronouncing 70 Saudi Arabic words. The results showed that guttural consonants have an influence on the vowel /a/ by lowering and backing it when they are adjacent and in the same syllable, while the vowel /a/ in the nonguttural consonants is raising and fronting their adjacent vowel /a/ in the same syllable in comparison with the vowel /a/ in the guttural environment.
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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 | 0.002 |
| 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 | 0.001 | 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".