Phonetic variability of stops and flaps in spontaneous and careful speech
Bibliographic record
Abstract
Variability is perhaps the most notable characteristic of speech, and it is particularly noticeable in spontaneous conversational speech. The current research examines how speakers realize the American English stops /p, k, b, g/ and flaps (ɾ from /t, d/), in casual conversation and in careful speech. Target consonants appear after stressed syllables (e.g., "lobby") or between unstressed syllables (e.g., "humanity"), in one of six segmental/word-boundary environments. This work documents the degree and types of variability listeners encounter and must parse. Findings show greater reduction in connected and spontaneous speech, greater reduction in high frequency phrases (but not within high frequency words), and greater reduction between unstressed syllables than after a stress. Although highly reduced productions of stops and flaps occur often, with approximant-like tokens even in careful speech, reduction does not lead to a large amount of overlap between phonological categories. Approximant-like realizations of expected stops and flaps in some conditions constitute the majority of tokens. This shows that reduced speech is something that listeners encounter, and must perceive, in a large proportion of the speech they hear.
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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 | 0.004 |
| 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 | 0.001 |
| 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".