The perception of formant-frequency range is affected by veridical and judged fundamental frequency
Bibliographic record
Abstract
The vowels produced by different speakers vary in terms of their fundamental frequency (f0) and formant frequencies (FFs). Variation in the production of a given vowel category between speakers of different sizes is primarily according to a single multiplicative parameter (related to speaker vocal-tract length). This parameter, which we refer to as FF-scaling, has an associated perceptual quality that listeners may use to determine apparent speaker characteristics and vowel quality. In a previous experiment [Barreda and Nearey, J. Acoust. Soc. Am. 129, 2661 (2011)], listeners were trained to identify a limited set of voices based on FF-scaling and f0 differences. The current study presented listeners with large number of voices (n = 4000) varying in FF-scaling and f0, arranged in a two-dimensional space where one dimension corresponded to each acoustic characteristic. Listeners were played a voice, and asked to indicate its location on the board, thereby providing an f0 and FF-scaling estimate for the voice. Results indicate that listeners are able to identify voice FF-scaling, and that this decision is informed primarily by veridical voice FF-scaling. However, there is a complicated relationship between perceived f0 and FF-scaling, suggesting an interdependent relationship in the perception of these characteristics.
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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 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".