Training listeners to report the acoustic correlate of formant-frequency scaling using synthetic voices
Bibliographic record
Abstract
The vocal tract length of a speaker is the primary determinant of the range of formant frequencies (FFs) produced by that speaker. Listeners have demonstrated sensitivity to the average FFs produced by voices, for example, in estimating the relative heights of two speakers based on their speech. However, it is not known whether they can learn to identify voices based on the acoustic characteristic associated with the average FFs produced by a voice (this characteristic will be referred to as FF-scaling). To investigate this, a series of vowels corresponding to voices that differed in their average f0 and/or FF-scaling were synthesized. Listeners (n = 71) were trained to identify these voices using a training procedure where, for each trial, they heard the vowels representing a voice and then had to identify the stimulus voice from among a series of candidate voices that differed in terms of their FF-scaling and/or their f0. Results indicate that listeners can identify voices on the basis of FF-scaling quite accurately and consistently after only a short training session and that, although f0 weakly influences these estimates, they are most strongly determined by the stimulus FFs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".