Design and Performance Analysis of Bayesian, Neyman–Pearson, and Competitive Neyman–Pearson Voice Activity Detectors
Bibliographic record
Abstract
In this paper, the Bayesian, Neyman-Pearson (NP), and competitive Neyman-Pearson (CNP) detection approaches are analyzed using a perceptually modified Ephraim-Malah (EM) model, based on which a few practical voice activity detectors are developed. The voice activity detection is treated as a composite hypothesis testing problem with a free parameter formed by the prior signal-to-noise ratio (SNR). It is revealed that a high prior SNR is more likely to be associated with the ldquospeech hypothesisrdquo than the ldquopause hypothesisrdquo and vice versa, and the CNP approach exploits this relation by setting a variable upper bound for the probability of false alarm. The proposed voice activity detectors (VADs) are tested under different noises and various SNRs, using speech samples from the Switchboard database and are compared with adaptive multirate (AMR) VADs. Our results show that the CNP VAD outperforms the NP and Bayesian VADs and compares well to the AMR VADs. The CNP VAD is also computationally inexpensive, making it a good candidate for applications in communication systems.
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How this classification was reachedexpand
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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".