The Development of Voiceless Sibilant Fricatives in Putonghua-Speaking Children
Bibliographic record
Abstract
PURPOSE: The aims of the present study are (a) to quantify the developmental sequence of fricative mastery in Putonghua-speaking children and discuss the observed pattern in relation to existing theoretical positions, and (b) to describe the acquisition of the fine-articulatory/acoustic details of fricatives in the multidimensional acoustic space. METHOD: Twenty adults and 97 children participated in a speech-production experiment, repeating a list of fricative-initial words. Two independent measures were applied to quantify the relative sequence of fricative acquisition: auditory-based phonetic transcription and acoustics-based statistical modeling. Two acoustic parameters-fricative centroid frequency and F2 onset-were used to index tongue-body and tongue-tip development, respectively. RESULTS: Both transcription and statistical modeling of acoustics yielded the sequence of /ɕ/ ⟶ /ʂ/ ⟶ /s/. Acoustic analysis further revealed gradual separation in both acoustic dimensions, with the initial undifferentiated form ambiguous between /ɕ/ and /ʂ/. CONCLUSIONS: The observed sound-acquisition order was interpreted as reflecting a combined influence of both oromotor maturation and language-specific phoneme frequency in Putonghua. Acoustic results suggest a maturational advantage of the tongue body over the tongue tip during fricative development.
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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 | 0.001 | 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".