Prosodic prominence in a stressless language: An acoustic investigation of Indonesian
Bibliographic record
Abstract
Although it has been proposed that all languages may have some lexical stress property, recent studies of (Standard) Indonesian have concluded, based primarily on perception, that lexical stress is not present in this language. While it is philosophically problematic to prove the non-existence of a phenomenon, we examine data from a large-scale production study for both direct and indirect evidence of stress, contributing to the growing body of literature in this field. In the first case, evidence is sought that indicates that a particular syllable in a word exhibits acoustic properties typically associated with prominence (i.e. fundamental frequency (f0), duration, intensity, vowel quality). In the second case, evidence of enhancement of these properties on a particular syllable under focus is sought, for a more abstract stress property that is not overtly manifested at the word level. Although we find no evidence of lexical prominence, we observe acoustic patterns consistent with a higher level prominence corresponding to focus, manifested by strong (Intonational Phrase) boundary properties. Overall, our findings reveal that there is strong support for a class of languages lacking lexical stress, and in the absence of a stressed syllable to enhance, focus may be manifested prosodically as boundary properties.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".