Correlation Based Glimpse Proportion Index
Bibliographic record
Abstract
The glimpse proportion (GP) index is an objective intelligibility measure (OIM) based on the glimpse model of speech perception in noise. GP uses local SNR as a criterion to identify time-frequency (TF) regions, or glimpses, that are dominated by speech. Although GP has demonstrated high performance in predicting intelligibility in the presence of stationary and fluctuating noise, its application is limited to additive noise conditions. To address this drawback, we propose a correlation based GP (CGP) index that operates in the TF domain similar to GP but can be applied to a wider range of conditions. The proposed measure is optimized and evaluated using 16 subjective datasets involving speech corrupted by modulated noise, nonlinear processing, and reverberation. The results show that CGP has consistent high performance across all degradation conditions and, on average, outperforms several baseline OIMs. Additionally, CGP has low complexity and takes substantially less time to execute compared to baseline OIMs.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".