The acquisition of voiceless sibilant fricatives in children speaking Mandarin Chinese
Bibliographic record
Abstract
The current study aims to describe Mandarin-speaking children's acquisition of voiceless sibilant fricatives, /s/, /ʂ/, and /ɕ/, as assessed by acoustics. Forty children, aged 2–5, participated in a word repetition task. The stimuli were fricative-initial words that are familiar to children. Children's speech sound productions were recorded and analyzed spectrally. Two acoustic parameters were obtained: the centroid frequency calculated over the middle 40-ms slice of the fricative noise spectrum and onset F2 frequency, the second formant frequency taken at the onset of the vowel following target fricatives. Centroid frequency indexes where the major lingual constriction is made in the oral cavity and is inversely related to the length of the front resonating cavity during the articulation of voiceless sibilant fricatives. Onset F2 frequency indexes how the major constriction is made and is sensitive to the lingual posture during constriction. These two parameters have been demonstrated to capture adults' fricative distinctions successfully. The results indicated an early separation between /ʂ/ and the other two fricatives in the centroid dimension, and an early separation between /ɕ/ and other two fricatives in the onset F2 dimension, The results suggested that children gradually implement their motor control in different articulation/acoustic dimensions.
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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) | 0.001 | 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".