Spontaneous speech variability across languages: Labial and velar stops
Bibliographic record
Abstract
Stops such as /ptkbdg/ are perhaps the most-studied type of consonant in all of phonetics, and they have well-defined acoustic properties that one expects to find in a typical pronunciation. However, casual spontaneous speech reveals highly variable realizations of stops, ranging from voiceless stops with silent closure, burst, and aspiration noise, to weak approximants with only a slight weakening of formants, to deletion. Even careful speech reveals considerable variability. We examine acoustic realizations of intervocalic stops in Dutch, Spanish, Japanese, and English, as well as the L2 English speech of the native Dutch, Spanish, and Japanese speakers. For each speaker, we measure data from spontaneous casual conversation and from careful word-list reading. In this presentation, we focus on realizations of /pbkg/. Preliminary results indicate that Dutch speakers variably transfer word-final devoicing of Dutch voiced stops to their English /bg/, but that they do not rely on the Dutch /x/ (orthographic “g”) as a source of their English /g/. Results also show that Spanish /bg/ in conversation are almost categorically approximants or nearly deleted. Spanish speakers, especially those who learned English late, appear to apply this pronunciation to English consonants as a reduced speech style.
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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 | 0.005 |
| 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 | 0.001 |
| 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".