Correlation coefficient-based voice activity detector algorithm
Bibliographic record
Abstract
A voice activity detector (VAD) is an algorithm able to distinguish the speech regions from the background noise of the input signal and is an important step in many speech processing applications. The varying nature and the large variety of speech and background noise make this problem difficult especially for low signal to noise ratio (SNR) that is the case for many practical applications. In this paper we propose a new VAD algorithm designed to improve the solution of word boundary detection problem for variable background noise level in a real time application. The input signal is windowed in time domain and then the energy and the spectrum of the current frame are obtained. The first few frames are supposed not to contain speech and are used to obtain a first estimate of the noise parameters. These parameters are updated during the silence periods using a first order autoregressive filter. In order to obtain robust parameters that do not depend on the amplitude of the spectrum, the correlation coefficient of the instantaneous spectrum and an average of the background noise spectrum is calculated. The speech regions may be detected based on a statistical approach using a simple binary Markov model for speech activity process. To evaluate the performance of the proposed method a clean speech dataset from the TIMIT database corrupted with different types of noise from NOISEX database for different SNR levels has been utilized.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".