Multi-modal cross-linguistic perception of fricatives in clear speech
Bibliographic record
Abstract
Research shows that acoustic modifications in clearly enunciated fricative consonants (relative to the plain, conversational productions) facilitate auditory fricative perception, particularly for auditorily salient sibilant fricatives and for native perception. However, clear-speech effects on visual fricative perception have received less attention. A comparison of auditory and visual (facial) clear-fricative perception is particularly interesting since sibilant fricatives in English are more auditorily salient while non-sibilants are more visually salient. This study thus examines clear-speech effects on multi-modal perception of English sibilant and non-sibilant fricatives. Native English perceivers and non-native (Mandarin, Korean) perceivers with different fricative inventories in their native languages (L1s) identified clear and conversational fricative-vowel syllables in audio-only, visual-only, and audio-visual (AV) modes. The results reveal an overall positive clear-speech effect when visual information is involved. Considering the factor of AV saliency, clear speech benefits sibilants more in the auditory domain and non-sibilants more in the visual domain. With respect to language background, non-native (Mandarin and Korean) perceivers benefit from visual as well as auditory information, even for fricatives non-existent in their respective L1s, but the patterns of clear-speech gains are affected by the relative AV weighting and "nativeness" of the fricatives. These findings are discussed in terms of how saliency-enhancing and category-distinctive cues of speech sounds are adopted in AV perception to improve intelligibility.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".