The third-order cumulant of speech signals with application to reliable pitch estimation
Bibliographic record
Abstract
This paper provides a formal framework for using the third-order statistics (TOS) of speech signals and presents a new method for estimating the pitch and making voicing decision using the 3rd-order cumulant of the LPC residual. Analytical expressions for the horizontal slice of the 3rd-order cumulant as well as the kurtosis of voiced speech are derived using the McAulay sinusoidal model (McAulay et al., 1986). The derivations demonstrate that the skewness of voiced speech is sufficiently distinct from that of Gaussian noise and can be used to aid in detecting voicing. It is also shown that the 3rd-order cumulant slice has distinct characteristics in terms of periodicity, phase and harmonic content and is a reliable candidate for estimating the pitch. Actual speech data is used to verify the derivations and experimental results using Gaussian and street noise are used to demonstrate the performance in noisy conditions.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".