On the Competitive Neyman-Pearson Approach for Composite Hypothesis Testing and its Application in Voice Activity Detection
Bibliographic record
Abstract
The problem of composite hypothesis testing where the probability law governing the generation of the free parameter is not explicitly known is considered. It is shown that unlike the Neyman-Pearson (NP) approach, the competitive NP (CNP) approach models incomplete prior information about the source into the detector design by setting a variable upper bound for the probability of false-alarm term. Further, the CNP and NP approaches are employed to develop the CNP and NP detectors for voice activity detection (VAD), where the prior SNR is shown to be the free parameter of the composite hypothesis. We test the CNP and NP detectors using speech samples from the SWITCHBOARD database which are suitably corrupted using different noises and various SNRs. Our simulation results show that the CNP detector outperforms its NP counterpart and is comparable to the adaptive multi-rate (AMR) VADs
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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 | 0.023 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".