The role of tone and phrasing in the occurrence of period doubling and vocal fry in Mandarin
Bibliographic record
Abstract
Period doubling, an under-studied yet frequently-occurring subtype of creaky voice, has distinct acoustic and phonatory properties compared to vocal fry, the most-studied and known subtype of creaky voice. Little is known about their distributional patterns across tones or utterances, let alone their potentially different functions in informing linguistic meaning and categories. In this paper, I investigate the tonal and phrasal influences on the distribution of these two voicing types as they occur sub-phonemically in Mandarin Chinese. The results show that both creak subtypes occur most frequently in Tones 3 and 2, and period doubling is more widespread across tones than vocal fry. Period doubling occurs most frequently at utterance edges, with its frequency gradually increasing toward the end of utterances, possibly reflecting vocal instability. Vocal fry, in contrast, is concentrated in the post- and pre-focal positions conditioned by the sentence-medial stimuli and in utterance-final positions, suggesting a stronger linguistic role in marking weak prosodic elements. This study also discusses implications for speech production and linguistic functions of different kinds of creak.
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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) | 0.001 | 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".