“Probably, OK, whatever!”: Variability in conversational speech stops and flaps.
Bibliographic record
Abstract
In casual, conversational speech, speakers often do not fully realize all segments one would normally expect in a given word. For example, “better” may be pronounced with an approximant instead of a medial flap (if the tongue fails to make a complete closure at the alveolar ridge), the expected flap may be entirely deleted, or the segments may be so heavily overlapped that it is almost impossible to say which segments are present and which not. The current work investigates how much reduction occurs (including both changes and deletions), comparing casual conversations, connected reading, and isolated word-list reading. It compares the phonemes /p, t, k, b, d, g/, post-stress vs inter-unstressed, and six segmental environments. The results represent 13 native English speakers and comprise a dataset of more than 9000 tokens, with several acoustic measures. This provides very thorough data on phonetic variability in both natural and “lab” speech. The work finds relatively little effect of stress and complex effects of segmental environment attributable to places of articulation. It also provides insight into how variable speakers are in how much they reduce, both within a speaker and within the population, a topic not often addressed in phonetic research.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".