Occlusion effect on compensatory formant production and voice amplitude in response to real-time perturbation
Bibliographic record
Abstract
The importance of auditory feedback for controlling speech articulation has been substantiated by the use of the real-time auditory perturbation paradigm. With this paradigm, speakers receive their own manipulated voice signal in real-time while they produce a simple speech segment. In response, they spontaneously compensate for the manipulation. In the case of vowel formant control, various studies have reported behavioral and neural mechanisms of how auditory feedback is processed for compensatory behavior. However, due to technical limitations such as avoiding an electromagnetic artifact or metal transducers near a scanner, some studies require foam tip insert earphones. These earphones occlude the ear canal, and may cause more energy of the unmanipulated first formant to reach the cochlea through bone conduction and thus confound the effect of formant manipulation. Moreover, amplification of lower frequencies due to occluded ear canals may influence speakers' voice amplitude. The current study examined whether using circumaural headphones and insert earphones would elicit different compensatory speech production when speakers' first formant was manipulated in real-time. The results of the current study showed that different headphones did not elicit different compensatory formant production. Voice amplitude results were varied across different vowels examined; however, voice amplitude tended to decrease with the introduction of F1 perturbation.
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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) | 0.001 | 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".