Effects of reliable spectral-temporal characteristics of listening context on perception of monophthongs and diphthongs
Bibliographic record
Abstract
Brief experience with reliable characteristics of listening context, such as a precursor sentence, alters perception of subsequent vowel sounds [M. Kiefte and K. Kluender, J. Acoust. Soc. Am. 112, 2248 (2002)]. For example, an appropriately filtered precursor sentence serves to perceptually cancel effects of second formants (F2) of vowel sounds, forcing listeners to rely solely on spectral tilt for identification. Auditory systems effectively absorb spectral characteristics, including formant peaks, if those characteristics are reliable (redundant) properties of the listening context. Present studies investigate whether this perceptual calibration extends to reliable spectral-temporal properties. Listeners identified a series of stimuli with incremental increases in slope of F2 that varied perceptually from /u/ to /ui/. When repeated formant transitions, exactly matching those of the target vowels, were added to precursor sentences, /i/ percepts predominated despite no stimuli being appropriate for /i/ in isolation. When repeated formant transitions were reversed in precursors (high to low frequency), the same results were obtained. Because perceptual absorption of reliable acoustic characteristics was indifferent to F2 trajectory, it appears that processes that calibrate to reliable properties of a listening context are sensitive only to spectral, not temporal, composition of listening context. [Work supported by SSHRC and NIDCD.]
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".