Iterative Noise-Compensated Method to Improve LPC Based Speech Analysis
Bibliographic record
Abstract
It is well known that linear predictive coding (LPC) performs well when the prediction coefficients are estimated from noise-free speech, and the system tends to degrade and perform poorly on noisy speech. This paper describes a method to minimize the degradation on the prediction coefficients in the presence of noise when an LPC analysis is used. In this method, a more accurate estimation of noise power is computed by using a simplified noise power spectral density (PSD) estimator. After an inverse discrete Fourier transform (DFT), the extracted noise autocorrelation coefficients are gradually subtracted from the coefficients derived from noisy speech according to an iterative processing scheme. The proposed processing scheme takes the absolute value of the estimated reflection coefficients as the decision criterion. It is shown that performing this iterative procedure on every autocorrelation lag ensures a substantial decrease in the degrading effects of noise, while the estimated autocorrelation matrix is guaranteed to be positive-definite. Experimental results indicate that the variance of the estimated prediction coefficients can be decreased significantly using the proposed method.
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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 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".