A cross-modal account for synchronic and diachronic patterns of /f/ and /θ/ in English
Bibliographic record
Abstract
Abstract While the role of auditory saliency is well accepted as providing insight into the shaping of phonological systems, the influence of visual saliency on such systems has been neglected. This paper provides evidence for the importance of visual information in historical phonological change and synchronic variation through a series of audio-visual experiments with the /f/∼/θ/ contrast. /θ/ is typologically rare, an atypical target in sound change, acquired comparatively late, and synchronically variable in language inventories. Previous explanations for these patterns have focused on either the articulatory difficulty of an interdental tongue gesture or the perceptual similarity /θ/ shares with labiodental fricatives. We hypothesize that the bias is due to an asymmetry in audio-visual phonetic cues and cue variability within and across talkers. Support for this hypothesis comes from a speech perception study that explored the weighting of audio and visual cues for /f/ and /θ/ identification in CV, VC, and VCV syllabic environments in /i/, /a/, or /u/ vowel contexts in Audio, Visual, and Audio-Visual experimental conditions using stimuli from ten different talkers. The results indicate that /θ/ is more variable than /f/, both in Audio and Visual conditions. We propose that it is this variability which contributes to the unstable nature of /θ/ across time and offers an improved explanation for the observed synchronic and diachronic asymmetries in its patterning.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".