An Approach for Voiced/Unvoiced Decision of Colored Noise-Corrupted Speech
Bibliographic record
Abstract
A two-step algorithm for the voiced/unvoiced (V/UV) decision of colored noise-corrupted speech is presented in this paper. An effective noise-whitening process is first applied to the noisy speech to combat the adverse effect of colored noise. Then, a harmonicity measure is proposed which is derived from the LP residual of the pre-whitened speech. Integrating root-mean-square energy and zero-crossing rate, another composite measure is introduced. In the first step, signal-dependent initial-thresholds (SDITs) for both the measures which are capable of highlighting distinctive attributes of voiced and unvoiced frames, are determined analyzing their statistical properties. In the second step, based on the SDITs, a bi-feature logical target function is formulated to attain a preliminary score of V/UV decision. Additional voicing criteria are developed to conquer the artifacts that may exist due to the overlapping between decision regions. Simulation results demonstrate that the proposed algorithm yields superior performance in comparison with some of the existing V/UV decision schemes in the same interfering colored noise scenario.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".