<i>F</i>0 and the voicing states of initial consonants in Mandarin
Bibliographic record
Abstract
Studies have shown inconsistent F0 changes following the plain voiceless stops (F0PLAIN) compared to (1) voiced stops (F0VOICED) and (2) aspirated voiceless stops (F0ASPIRATE). Aerodynamic, laryngeal setting and auditory enhancement models of [±voice] have all been implicated in F0 variations [J. Kingston and R. L. Diehl, Language 70, 419–454 (1994)], but each explains only certain aspects of the prevocalic voicing effects on F0. Studies in English show that F0 of a vowel is simultaneously affected by multiple phonetic factors, such as voicing state, vowel identity, intonation, and place of articulation. Previous small-scale experiments in Mandarin also exhibit interactions of the phonetic distinctions on F0, but only certain aspects of the contextual variations of F0 have been examined. This study carries out a large-scale recording of Mandarin CV syllables in isolation, examining the interactive patterns of acoustic correlates of the voicing states of initial consonants. Preliminary results show dramatically different F0 changes from previous studies in Mandarin [C. X. Xu and Y. Xu, JIPA, 33, 165–181 (2003)] but show similar patterns to English, such that F0PLAIN is similar to F0VOICED (nasals), with F0ASPIRATE significantly higher. This study provides a better understanding of the cross-linguistic phonetic realization of voicing distinctions.
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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".