Speech rate influences categorical variation of English flaps and taps during normal speech
Bibliographic record
Abstract
This research demonstrates that North American English speakers with faster syllable iterance rates are more likely to produce reduced /t/ and /d/ as taps, which have two directions of motion, than flaps, which have one. Using B/M mode ultrasound to capture tongue motion direction, coupled acoustic recordings to capture syllable speed, we identified categorical variants of flaps and taps produced by 18 speakers and compared the average likelihood of each variant in relation to the rate at which speakers can repeat `ta'. Faster iterators produce more alveolar taps in single flap phrases (e.g. `autumn), and double flap phrases (e.g. `edit a'). Alveolar taps require more changes in direction of tongue motion, and are often produced to avoid articulatory conflicts with neighbouring vowels. The results support an argument that individual variation in flap/tap production is partly due to a motor skill constraint limiting cycles of tongue motion, weighed against avoiding articulatory conflict. Currently a second experiment, testing whether changing actual speech rates will generate categorical shifts in flap variant production, is in progress. Preliminary results are expected by the conference date.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".