Effects of Knowledge of Task on Control of Oral-Nasal Balance in Speech
Bibliographic record
Abstract
INTRODUCTION: Previous research has shown that altering the nasal signal level auditory feedback changed the control of oral-nasal balance in normal speakers. The present study investigated whether knowledge of the task and the instruction not to compensate would change the participants' response to the manipulation. METHODS: Twenty participants (10 females) in 2 groups continuously repeated a sentence while their nasal signal level was increased or decreased and fed back to them via headphones, so the speakers heard themselves as more or less nasal, respectively. After the first recording session, participants were debriefed about the true nature of the experiment. They were instructed not to compensate in the second recording session. The outcome measures were the percentage changes of nasalance scores from the first baseline. RESULTS: Statistical analysis using a repeated measures analysis of variance showed an effect of the nasal signal level, F(5,80) = 2.51, p = 0.049, and a nasal signal level by knowledge of task interaction effect, F(5,80) = 3.25, p = 0.019. Post hoc tests showed that the maximum nasal signal level auditory feedback resulted in a significant decrease of nasality from the initial baseline. CONCLUSION: Despite knowledge of the task, speakers were unable to resist compensating. As found in previous research, there was a numerically higher compensation response at the maximum than at the minimum nasal signal level auditory feedback condition.
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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 | 0.001 | 0.010 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".