Automatic classification of English fricatives using cepstral coefficients
Bibliographic record
Abstract
We use a classification tool previously tested on Romanian fricatives to categorize the front (non-sibilant) fricatives of English by place of articulation. Labiodental and interdental fricatives are difficult to distinguish acoustically, posing problems even for human perception. Prior classification work with English front fricatives has not been very successful with this contrast, with correct classification rates ranging from 40 to 60%. The feature set we use for coding the acoustic properties of the fricatives and their following vowels comprise the first six cepstral coefficients (c0–c5). The acoustic features are measured at 10-ms intervals across each segment; the measures obtained are then binned into three contiguous intervals for both the fricative and the vowel, representing the onset, steady state, and offset of each segment. The boundaries between regions are set by using a hidden Markov model to determine three internally uniform regions with respect to their acoustic properties. The mean value of each acoustic feature within each region is obtained; thus, each CV production yields six measurements per coefficient. We are testing this model on fricatives from the TIMIT corpus and classifying them using multinomial logistic regression models. While this investigation is underway, we expect higher correct classification rates than previous work.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".