A Formant Frequency Estimation Scheme for Speech Signals in the Presence of Noise
Bibliographic record
Abstract
In this paper, a new scheme for the estimation of formant frequencies of noise-corrupted speech signals is presented. A once-repeated autocorrelation function (ORACF) of the observed noisy speech signal is proposed to employ in a linear predictive based formant estimation method. It has been shown that the ORACF is capable of reducing the effect of additive noise significantly and if, instead of conventional ACF, ORACF is used in a modified form of least-squares Yule-Walker equations, a better performance in the formant estimation is achieved. Moreover, a frequency-domain algorithm is incorporated in the proposed scheme to avoid the possible estimation error in extracting a formant with low energy. The proposed algorithm has been tested on synthetic and natural vowels as well as some naturally spoken sentences in the presence of additive noise. The experimental results demonstrate a better performance obtained by the proposed scheme in comparison to some of the existing methods at low levels of signal-to-noise ratio (SNR).
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".