Visual feedback and self-monitoring in speech learning via hand movement
Bibliographic record
Abstract
While human speech learning largely relies on perceiving sounds [Brainard and Doupe, Nat. Rev. Neurosci. 1(1), 31–40 (2000)], being able to see articulators also contributes to learning speech sounds [Gick et al. (2008), Phonology Second Language Acquisition 36, 315–328 (2000)]. However, seeing hands is not necessarily helpful for learning new hand movements [Emmorey et al., J. Memory Lang., 61(3), 398–411 (2009)]. This study investigates whether being able to see a hand, acting as a speech articulator, facilitates the learning of speech production via hand movements. Two groups of participants under different visual feedback conditions were asked to produce different vowel sequences via a system that maps hand movements to F1 and F2 of English vowels [Liu et al., Can. Acoust. 48(1) (2020)]. The results suggest that visual feedback contributes to the speed of speech learning and reaching vowel targets more accurately via hand movements. This study provides insight on the importance of visual feedback in monitoring speech, and supports the view that monitoring speech articulators visually can accelerate speech learning.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".