Influence of Altered Auditory Feedback on Oral–Nasal Balance in Speech
Bibliographic record
Abstract
Purpose: This study explored the role of auditory feedback in the regulation of oral-nasal balance in speech. Method: Twenty typical female speakers wore a Nasometer 6450 (KayPentax) headset and headphones while continuously repeating a sentence with oral and nasal sounds. Oral-nasal balance was quantified with nasalance scores. The signals from 2 additional oral and nasal microphones were played back to the participants through the headphones. The relative loudness of the nasal channel in the mix was gradually changed so that the speakers heard themselves as more or less nasal. An additional amplitude control group of 9 female speakers completed the same task while hearing themselves louder or softer in the headphones. Results: A repeated-measures analysis of variance of the mean nasalance scores of the stimulus sentence at baseline, minimum, and maximum nasal feedback conditions demonstrated a significant effect of the nasal feedback condition. Post hoc analyses found that the mean nasalance scores were lowest for the maximum nasal feedback condition. The scores of the minimum nasal feedback condition were significantly higher than 2 of the 3 baseline feedback conditions. The amplitude control group did not show any effects of volume changes on nasalance scores. Conclusions: Increased nasal feedback led to a compensatory adjustment in the opposite direction, confirming that oral-nasal balance is regulated by auditory feedback. However, a lack of nasal feedback did not lead to a consistent compensatory response of similar magnitude.
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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) | 0.000 | 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".