Exposure to Canadian French Cued Speech Improves Consonant Articulation in Children With Cochlear Implants: Acoustic and Articulatory Data
Bibliographic record
Abstract
PURPOSE: One of the strategies that can be used to support speech communication in deaf children is cued speech, a visual code in which manual gestures are used as additional phonological information to supplement the acoustic and labial speech information. Cued speech has been shown to improve speech perception and phonological skills. This exploratory study aims to assess whether and how cued speech reading proficiency may also have a beneficial effect on the acoustic and articulatory correlates of consonant production in children. METHOD: Eight children with cochlear implants (from 5 to 11 years of age) and with different receptive proficiency in Canadian French Cued Speech (three children with low receptive proficiency vs. five children with high receptive proficiency) are compared to 10 children with typical hearing (from 4 to 11 years of age) on their production of stop and fricative consonants. Articulation was assessed with ultrasound measurements. RESULTS: The preliminary results reveal that cued speech proficiency seems to sustain the development of speech production in children with cochlear implants and to improve their articulatory gestures, particularly for the place contrast in stops as well as fricatives. CONCLUSION: This work highlights the importance of studying objective data and comparing acoustic and articulatory measurements to better characterize speech production in children.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".