The aerodynamic puzzle of nasalized fricatives: Aerodynamic and perceptual evidence from Scottish Gaelic
Bibliographic record
Abstract
Abstract Scottish Gaelic is sometimes described as having nasalized fricatives (/ṽ/ distinctively, and [f̃, x̃, h̃], etc. through assimilation). However, there are claims that it is not aerodynamically possible to open the velum for nasalization while maintaining frication noise. We present aerodynamic data from 14 native Scottish Gaelic speakers to determine how the posited nasalized fricatives in this language are realized. Most tokens demonstrate loss of nasalization, but nasalization does occur in some contexts without aerodynamic conflict, e.g., nasalization with the consonant realized as an approximant, nasalization of [h̃], nasalization on the preceding vowel, or sequential frication and nasalization. Furthermore, a very few tokens do contain simultaneous nasalization and frication with a trade-off in airflow. We also present perceptual evidence showing that Gaelic listeners can hear this distinction slightly better than chance. Thus, instrumental data from one of the few languages in the world described as having nasalized fricatives confirms that the claimed sounds are not made by producing strong nasalization concurrently with clear frication noise. Furthermore, although speakers most often neutralize the nasalization, when they maintain it, they do so through a variety of phonetic mechanisms, even within a single language.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".