Evaluation of a strategy for automatic formant tracking
Bibliographic record
Abstract
Variations on an automatic formant tracking strategy developed at Alberta will be compared to manual formant measurements from two databases of vowels spoken by men, women, and children (in Texas or Michigan). ‘‘Correct’’ vowel formant candidates for F1, F2, and F3 may be found roughly 85–90 percent of the time for adult male speakers using autocorrelation LPC with the following settings: F3 maximum at 3000 Hz, LPC order of 14, sampling rate of 10 kHz [J. Markel and A. Gray, Linear Prediction of Speech (Springer, New York, 1975)]. Experience shows good results are also often found with females’ and children’s speech, provided the sampling rate and F3 maximum are scaled appropriately for each speaker. Our new basic strategy involves analyzing each utterance at several distinct sampling rates and coordinated F3 cutoff frequencies with a fixed LPC order. Each scaling choice provides an independent set of candidates that is post-processed by a simple tracking algorithm. A correlation measure between a spectrogram of the original signal and one resynthesized from each estimated track set is defined. This measure is combined with other heuristic figures of merit (based on, e.g., continuity, formant ranges, and bandwidths) to choose the ‘‘best’’ analysis.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".