Bibliographic record
Abstract
Information normally associated with pitch, such as intonation, can still be conveyed in whispered speech despite the absence of voicing. 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., M. Kiefte, J. Acoust. Soc. Am. 116, 2546 (2004)]. However, spectral peak frequencies associated with formants have been identified as important correlates to vowel identity. Spectral peak frequencies may serve two roles in the perception of whispered speech: to indicate both vowel identity and intended pitch. Data will be presented to examine the relative importance of several acoustic properties including spectral peak frequencies and spectral shape parameters in both the production and perception of whispered vowels. Speakers were asked to phonate and whisper vowels at three different pitches across a range of roughly a musical fifth. It will be shown that relative spectral change is preserved within vowels across intended pitches in whispered speech. In addition, several models of vowel identification by listeners will be presented. [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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".