Bibliographic record
Abstract
It is well known that whispered speech is able to convey information that is normally associated with pitch. For example, it is possible to whisper the question ‘‘You are going today?’’ without any syntactic information to distinguish this sentence from a simple declarative. It has been shown that pitch change in whispered speech is correlated with the simultaneous raising or lowering of several formants [e.g., Kallail and Emanuel, J. Speech Hear. Res. 27, 245–251 (1984)]. Data will be presented from 81 native speakers of English from the Halifax region of Nova Scotia (35 men and 46 women) who were asked to phonate and whisper the vowels /i,I,e,ε,æ,≳,o,U,u,■,■,■I,aU,aI/ at three different pitches across a range of roughly a musical 5th. Formant frequency variability is much greater for whispered vowels with different intended pitches resulting in much greater between-category overlap. Listeners’ categorizations of these stimuli will be reported as well as results from a discriminant analysis based on either static or dynamic spectral information. [Work supported by SSHRC.]
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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 | 0.001 |
| 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".