Bibliographic record
Abstract
Tongue twisters present an interesting problem with respect to their implication to the interactions between phonology and phonetics. Only recently, however, have the articulations produced in tongue twisters been analyzed phonetically. The research presented is a preliminary study into the so-called /s/ → /∫/ neutralization occurring in English tongue twisters. Traditionally, it was believed that tongue slips in tongue twisters resulted in complete phoneme replacement, neutralizing the contrast. (Pronouncing “seashell” as “sheashell”). More recent studies suggest a differing phonetic account, in which the resulting sound is nearly-neutralized. This study examined the segments /s/ and /∫/ near-neutralizing in differing contexts. Acoustic data was collected from one speaker eliciting eight artificial tongue twisters repeatedly in various contexts. The central band of frequency of the sounds were analyzed using Praat. A near-neutralization effect was found, that the “neutralized" segment was significantly between a /s/ and a /sh/. This effect was observed in both a forwards and backwards direction (“sheashell” & “seasell”) were both present in the data, with a noticeably stronger right-to-left effect; in accordance with cross-linguistic studies of /s/ - /sh/ neutralization. A recurrent network articulatory model is presented in the discussion, which can account for the asymmetry and context sensitivity of results. Findings move us towards a greater understanding of the greater problem of sibilant harmony across languages.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".