The relation between production and perception of Mandarin tone
Bibliographic record
Abstract
This pilot study explores the potential production-perception relation of Mandarin tones by examining the association between the acoustic features of native Mandarin tone production and perception of such features, using Tone 2 (rising tone) as a testing case. The perception stimuli were resynthesized by varying F0 onset (spectral feature) and F0 turning-point location (temporal feature) orthogonally forming a grid of tone contours. The endpoints of these dimensions were determined by natural Tone 1 and 3 productions, presumably corresponding to perceptual boundaries for Tone 2. A parabola was fitted to the resynthesized tones to determine the overall shape of the tone contour. Native Mandarin participants were asked to identify the best exemplar of Tone 2. The acoustic analysis of Tone 2 productions by the same participants included measurements of the same features. Preliminary correlation analysis showed a positive production-perception correlation for the temporal feature of F0 turning-point location, but no significant correlation for the spectral features of F0 onset and F0 contour shape, indicating F0 turning-point location as a critical perceptual cue for Tone 2. Further research will include additional tones and additional acoustic features found to characterize these tones to identify the nature of production-perception relation of Mandarin tones.
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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 | 0.014 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".