Quantifying time-varying coordination of multimodal speech signals using correlation map analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper demonstrates an algorithm for computing the instantaneous correlation coefficient between two signals. The algorithm is the computational engine for analyzing the time-varying coordination between signals, which is called correlation map analysis (CMA). Correlation is computed around any pair of points in the two input signals. Thus, coordination can be assessed across a continuous range of temporal offsets and be detected even when changing over time due to temporal fluctuations. The correlation algorithm has two major features: (i) it is structurally similar to a tunable filter, requiring only one parameter to set its cutoff frequency (and sensitivity), (ii) it can be applied either uni-directionally (computing correlation based only on previous samples) or bi-directionally (computing correlation based on both previous and future samples). Computing instantaneous correlation for a range of time offsets between two signals produces a 2D correlation map, in which correlation is characterized as a function of time and temporal offset. Graphic visualization of the correlation map provides rapid assessment of how correspondence patterns progress through time. The utility of the algorithm and of CMA are exemplified using the spatial and temporal coordination of various audible and visible components associated with linguistic performance.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it