Speech articulator movements recorded from facing talkers using two electromagnetic articulometer systems simultaneously.
Bibliographic record
Abstract
Two 3-D electromagnetic articulometer systems, the Carstens AG500 and Northern Digital WAVE, have been used simultaneously without mutual interference to record the speech articulator movements of two talkers facing one another 2 m apart. A series of benchmark tests evaluating the stability of fixed distances between sensors attached to a rotating rigid body was first conducted to determine whether the two systems could operate independently, with results showing no significant effect of dual operation on either system. In the experiment proper, two native speakers of American English participated as subjects. Sensors were glued to three points on the tongue, the upper and lower incisors, lips, and left and right mastoid processes for each subject. Independent audio tracks were recorded using separate directional microphones, which were used to align the kinematic data from both subjects during post-processing. Data collected were of two types: extended spontaneous conversation and repeated incongruent word sequences (e.g., talker one produced “cop top...;” talker two “top cop...”). Both talkers show strong positive correlations between speech rate (in syllables/s) and head movement. The word sequences also show error and rate effects related to mutual entrainment. [Work supported by ARC Human Communication Science Network (RN0460284), MARCS Auditory Laboratories, NIH.]
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".