Acoustic characteristics of clearly spoken English tense and lax vowels
Bibliographic record
Abstract
Clearly produced vowels exhibit longer duration and more extreme spectral properties than plain, conversational vowels. These features also characterize tense relative to lax vowels. This study explored the interaction of clear-speech and tensity effects by comparing clear and plain productions of three English tense-lax vowel pairs (/i-ɪ/, /ɑ-ʌ/, /u-ʊ/ in /kVd/ words). Both temporal and spectral acoustic features were examined, including vowel duration, vowel-to-word duration ratio, formant frequency, and dynamic spectral characteristics. Results revealed that the tense-lax vowel difference was generally enhanced in clear relative to plain speech, but clear-speech modifications for tense and lax vowels showed a trade-off in the use of temporal and spectral cues. While plain-to-clear vowel lengthening was greater for tense than lax vowels, clear-speech modifications in spectral change were larger for lax than tense vowels. Moreover, peripheral tense vowels showed more consistent clear-speech modifications in the temporal than spectral domain. Presumably, articulatory constraints limit the spectral variation of these extreme vowels, so clear-speech modifications resort to temporal features and reserve the primary spectral features for tensity contrasts. These findings suggest that clear-speech and tensity interactions involve compensatory modifications in different acoustic domains.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".