Bibliographic record
Abstract
The perception of fricatives is not well understood, in part because they are generated with air turbulence, which complicates their articulatory and acoustic properties (e.g. Shadle 2012). Sibilant fricatives are especially difficult to characterize because, unlike other obstruents, “...place as well as manner cues are signaled primarily by the spectral structure of the segment itself’ (Toda et al. 2010:343), rather than by the formant transitions. While acoustic models of sibilants such as those of Howe & McGowan (2005) and Toda et al. (2010) continue to improve our understanding of these speech sounds, the precise organizational principles behind their perception remain unknown. Sibilant fricatives can be ordered along a one-dimensional continuum defined by the spectral mean. The spectral mean is inversely proportional to the volume of the sublingual cavity, so as the place of articulation approaches the anterior of the mouth, the spectral mean increases. While this simple representation captures the basic facts, it fails to account for more subtle distinctions, such as Fujisaki & Kunisaki’s (1978) finding that the Japanese alveolar fricative [s] is best modeled using a spectral distribution with two spectral peaks and one valley, rather than just a single peak. This study investigated whether or not certain pairs of sibilant and palatal fricatives were more difficult to differentiate than others. This was done by presenting listeners with synthesized stimuli in an AX discrimination task and recording their reaction times (RTs). The RTs from correct responses were then transformed and analyzed using multidimensional scaling (MDS) in order to reveal the relative perceptual distances between these fricatives.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".