Acoustic properties of word and phrasal prominence in Uzbek
Bibliographic record
Abstract
Based on a large-scale corpus of experimental data produced by 8 native speakers of Tashkent Uzbek, we assess the presence of canonical word-final stress in real words spoken in three dialogue types: without focus, with contrastive focus, and with new information focus on the target. The first context provides baseline information regarding the manifestation of stress, in the absence of additional focus properties. By comparing the latter two contexts with the former, we are also able to assess the acoustic manifestation of the two types of focus. The most noteworthy properties of the final syllable are its relatively long duration and sharp falling contour, potentially serving as the cues to lexical stress, and enhanced by both types of focus. Due to the word-final position of stress, however, the patterns we observe could also be consistent with boundary properties, a possibility we consider as well. In addition, we briefly compare the prosodic patterns we observe in Uzbek with similarly collected data in Turkish. We find that the prominence patterns in Uzbek, while not particularly strong, are nevertheless stronger than those in Turkish, and also exhibit crucial differences. Implications for Turkic prosody more generally are also suggested.
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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 | 0.002 |
| 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) | 0.003 | 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".