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Record W2110150626 · doi:10.3109/02699206.2013.862863

Transcription-based and acoustic analyses of rhotic vowels produced by children with and without speech sound disorders: Further analyses from the Memphis Vowel Project

2014· article· en· W2110150626 on OpenAlexaff
Hyunju Chung, Kathryn Farr, Karen Pollock

Bibliographic record

VenueClinical Linguistics & Phonetics · 2014
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsUniversity of Alberta
FundersNational Institute on Deafness and Other Communication Disorders
KeywordsDiphthongVowelMemphisPhonetic transcriptionTranscription (linguistics)PsychologySpeech recognitionLinguisticsVariation (astronomy)PhoneticsAudiologyComputer scienceBiologyMedicine

Abstract

fetched live from OpenAlex

The acquisition of rhotic monophthongs (/ɝ/ and /ɚ/) and diphthongs (/ɪ͡ɚ, ɛ͡ɚ, ɔ͡ɚ and ɑ͡ɚ/) was examined in 3- and 4-year-old children with and without speech sound disorders (SSDs), using both transcription-based and acoustic analyses. African-American (AA) and European-American (EA) participants (n = 40) with and without SSD were selected from archival data collected as part of the Memphis Vowel Project. Dialect variation influenced rhotic vowels differently for EA and AA children, thus their data were reported separately. Transcription-based analyses showed wide variation in the accuracy of different rhotic vowels. The most frequent error pattern for children with SSD was Derhoticization to a Back Rounded Vowel (e.g. /ɝ/ → [ʊ]; /ɪ͡ɚ/ → [ɪ͡о]). Rhotic diphthong reduction errors were less frequent; however, Coalesence (/ɑ͡ɚ/ → [ɔ]) was often observed for /ɑ͡ɚ/. F2, F3 and F3-F2 spectral movement patterns revealed differences between productions transcribed as correct and incorrect.

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)0.001
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
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 head0.109
GPT teacher head0.448
Teacher spread0.339 · 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.

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

Citations5
Published2014
Admission routes1
Has abstractyes

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