Exploring production-perception relationships in normal hearing and cochlear implant adults: A lip-tube perturbation study
Bibliographic record
Abstract
Is has been claimed that auditory feedback mechanisms enable monitoring and calibration of feedforward commands in speech production. Therefore, lack of auditory feedback may interfere with adequate compensation strategies in perturbed situations. This study investigates the effect of hearing status and a lip tube perturbation on vowel production. Eleven normal-hearing controls, and seventeen cochlear implant (CI) users (7 prelingually, 10 postlingually) were recorded during the production of the vowel /u/. Acoustic and articulatory analyses were conducted with and without a 15-mm-diam tube inserted between the lips. Recording sessions were also made before and after the perturbation, with and without auditory feedback. Deaf participants' auditory feedback was provided by the CI and interrupted by switching off their implant devices. Separate analyses were conducted on the acoustic parameters (first formant, second formant, and fundamental frequency) and on articulatory parameters. Results revealed a main effect of group and an interaction between condition and hearing status. Together, results suggest that auditory feedback plays an important role in speech compensation.
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 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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 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".