Interactions of speaking condition and auditory feedback on vowel production in postlingually deaf adults with cochlear implants
Bibliographic record
Abstract
This study investigates the effects of speaking condition and auditory feedback on vowel production by postlingually deafened adults. Thirteen cochlear implant users produced repetitions of nine American English vowels prior to implantation, and at one month and one year after implantation. There were three speaking conditions (clear, normal, and fast), and two feedback conditions after implantation (implant processor turned on and off). Ten normal-hearing controls were also recorded once. Vowel contrasts in the formant space (expressed in mels) were larger in the clear than in the fast condition, both for controls and for implant users at all three time samples. Implant users also produced differences in duration between clear and fast conditions that were in the range of those obtained from the controls. In agreement with prior work, the implant users had contrast values lower than did the controls. The implant users' contrasts were larger with hearing on than off and improved from one month to one year postimplant. Because the controls and implant users responded similarly to a change in speaking condition, it is inferred that auditory feedback, although demonstrably important for maintaining normative values of vowel contrasts, is not needed to maintain the distinctiveness of those contrasts in different speaking conditions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.001 | 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".