Cepstral coefficients successfully distinguish the front Greek fricatives
Bibliographic record
Abstract
In the current study, we explore the factors underlying the well-known difficulty in acoustic classification of front fricatives (McMurray & Jongman, 2011; Maniwa et al., 2009) by taking a closer look at the production of 29 native Greek speakers. Our corpus consists of Greek fricatives from five places of articulation and two voicing values [f, v, θ, ð, s, z, ç, ʝ, x, ɣ] produced in nonce disyllabic words before [a, o, u] in stressed syllables (e.g., [ˈθakos]). We apply a relatively novel classification method based on cepstral coefficients, previously successful with obstruent bursts (Bunnell et al., 2004), vowels (Ferragne & Pellegrino, 2010), and Romanian fricatives (Spinu & Lilley, 2016). Our method yields the best correct classification rates reported to date for front fricatives: Present study: 88%; English: 66% (Jongman et al., 2000), Greek: 55.1% (Nirgianaki, 2014). The important cues for the successful classification are the vowel following the target fricative and the second region of frication noise. Our study adds to the body of work aimed at identifying techniques for quantifying and categorizing large samples of speech. Obtaining higher classification rates than before takes us one step closer to understanding the properties of “difficult” sounds like the front fricatives.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".