Vowel acquisition by prelingually deaf children with cochlear implants
Bibliographic record
Abstract
Phonetic transcriptions (study 1) and acoustic analysis (study 2) were used to clarify the nature and rhythm of vowel acquisition following the cochlear implantation of prelingually deaf children. In the first study, seven children were divided according to their degree of hearing loss (DHL): DHL I: 90–100 dB of hearing loss, 1 children; DHL II: 100–110 dB, 3 children; and DHL III: over 110 dB, 3 children. Spontaneous speech productions were recorded and videotaped 6 and 12 months postsurgery and vowel inventories were obtained by listing all vowels that occurred at least twice in the child’s repertoire at the time of recording. Results showed that degree of hearing loss and age at implantation have a significant impact on vowel acquisition. Indeed, DHL I and II children demonstrated more diversified as well as more typical pattern of acquisition. In the second study, the values of the first and second formants were extracted. The results suggest evolving use of the acoustic space, reflecting the use of auditory feedback to produce the three phonological features exploited to contrast French vowels (height, place of articulation, and rounding). The possible influence of visual feedback before cochlear implant is discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".