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Record W2513884741 · doi:10.1044/2016_jslhr-s-14-0142

The Development of Voiceless Sibilant Fricatives in Putonghua-Speaking Children

2016· article· en· W2513884741 on OpenAlexafffund
Fangfang Li, Benjamin Munson

Bibliographic record

VenueJournal of Speech Language and Hearing Research · 2016
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsUniversity of Lethbridge
FundersUniversity of LethbridgeNational Institute on Deafness and Other Communication DisordersOhio State University
KeywordsPhonetic transcriptionAcousticsSpeech recognitionSpeech productionPhoneticsAcoustic spacePsychologyComputer scienceLinguisticsSound (geography)Physics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.768
Threshold uncertainty score0.260

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.086
GPT teacher head0.434
Teacher spread0.348 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations32
Published2016
Admission routes2
Has abstractyes

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