Effects of formant peak flattening on vowel perception: A larger-scale web-based experiment
Bibliographic record
Abstract
Ito et al. reported that suppressing the first or the second formant in Japanese synthesized vowels did not importantly change listener perception. However, quantitative analyses of data collected in the English (Nenadić et al., 2020) and the Serbian language (Nenadić et al., 2023) have shown that formant suppression increases response entropy between participants (i.e., decreases agreement in vowel identity). Previous studies never tested more than fifteen participants, but it was noted that certain listeners gave different responses to a manipulated synthesized vowel in comparison to its original version more often than others. We tested 118 native monolingual speakers of Serbian language (87% female, 13% male; age 18 to 44, M = 21.07, SD = 4.71) in an online replication of the experiment. The results again show that listeners agree less about vowel identity for stimuli with suppressed formants. Certain listeners again tended to change their response to the manipulated version of the vowel more often than others. Importantly, this tendency did not correlate with their mean response latency. We discuss possible causes for these findings, including differences in listener strategies and time requirements of a more careful processing of the incoming signal.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".