Bibliographic record
Abstract
Abstract Formant frequencies are the positions of the local maxima of the power spectral envelope of a sound signal. They arise from acoustic resonances of the vocal tract air column, and they provide substantial information about both consonants and vowels. In running speech, formants are crucial in signaling the movements with respect to place of articulation. Formants are normally defined as accumulations of acoustic energy estimated from the spectral envelope of a signal. However, not all such peaks can be related to resonances in the vocal tract, as they can be caused by the acoustic properties of the environment outside the vocal tract, and sometimes resonances are not seen in the spectrum. Such formants are called spurious and latent, respectively. By analogy, spectral maxima of synthesized speech are called formants, although they arise from a digital filter. Conversely, speech processing algorithms can detect formants in natural or synthetic speech by modeling its power spectral envelope using a digital filter. Such detection is most successful for male speech with a low fundamental frequency where many harmonic overtones excite each of the vocal tract resonances that lie at higher frequencies. For the same reason, reliable formant detection from females with high pitch or children’s speech is inherently difficult, and many algorithms fail to faithfully detect the formants corresponding to the lowest vocal tract resonant frequencies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.102 | 0.067 |
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".