Prosodic Cues for Broad, Narrow, and Corrective Focus in Persian
Bibliographic record
Abstract
Previous studies have demonstrated that focus significantly alters sentential prosody in Persian. However, research on the phonetic realization of non-corrective narrow focus is scarce compared to that on broad and corrective focus. This paper presents a systematic production study investigating whether Persian speakers distinguish between three focus structures on target words that bear a pitch accent, that is, broad, narrow, and corrective focus. In a multidimensional phonetic analysis, we investigated the parameters of intensity, duration, and F0. Taking a local perspective, results show that the duration of the target word is a robust cue for focus marking in both syllables of the word, exhibiting a three-step pattern (corrective > narrow > broad). In the first syllable, intensity is a reliable cue to distinguish broad focus from the other two focus types, with higher intensities in broad focus. In the accented syllable, a different two-step pattern is observed, with narrow and corrective focus showing larger F0 spans than broad focus. Taking a global perspective that considers the parts of the utterance before and after the target word, we find a lowering of F0 and decreased intensity for narrow and corrective focus in the pre-target region. In the post-target region, we find strong evidence for differences in mean F0 and intensity with lower F0 in corrective focus than in broad and narrow focus, while the intensity is lower in narrow and corrective focus than in broad focus. Our analysis deepens our understanding of Persian prosody.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".