Statistical Analysis of Doubletalk Detection for Calibration and Performance Evaluation
Why this work is in the frame
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Bibliographic record
Abstract
Doubletalk detection is an important part of a practical echo canceller implementation, but a difficult problem is calibrating the doubletalk detector for arbitrary environments and input signals. In this paper, it is shown that a statistical model of a doubletalk detection variable's probability density function (PDF) can be used to obtain an optimal detection threshold and expected detection performance curves. In particular, a statistical analysis of a recently proposed cross-correlation-based doubletalk detector is presented. The doubletalk detection variable is modeled in terms of its constituent parameter estimators, resulting in conditional PDFs in the absence and presence of doubletalk. These are used to obtain a signal-adaptive detection threshold for calibration, and to provide expected doubletalk detection probability. Simulations are presented comparing the theoretical and measured detection probability compared to a fixed detection threshold for speech input and doubletalk signals. The results indicate a close agreement with the proposed model for moderate-to-high levels of doubletalk
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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 | 0.000 | 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