Learning to produce speech with an altered vocal tract: The role of auditory feedback
Bibliographic record
Abstract
Modifying the vocal tract alters a speaker's previously learned acoustic-articulatory relationship. This study investigated the contribution of auditory feedback to the process of adapting to vocal-tract modifications. Subjects said the word /tas/ while wearing a dental prosthesis that extended the length of their maxillary incisor teeth. The prosthesis affected /s/ productions and the subjects were asked to learn to produce "normal" /s/'s. They alternately received normal auditory feedback and noise that masked their natural feedback during productions. Acoustic analysis of the speakers' /s/ productions showed that the distribution of energy across the spectra moved toward that of normal, unperturbed production with increased experience with the prosthesis. However, the acoustic analysis did not show any significant differences in learning dependent on auditory feedback. By contrast, when naive listeners were asked to rate the quality of the speakers' utterances, productions made when auditory feedback was available were evaluated to be closer to the subjects' normal productions than when feedback was masked. The perceptual analysis showed that speakers were able to use auditory information to partially compensate for the vocal-tract modification. Furthermore, utterances produced during the masked conditions also improved over a session, demonstrating that the compensatory articulations were learned and available after auditory feedback was removed.
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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 | 0.001 |
| 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".