Effects of clear speech and language background on multimodal perception of English fricatives
Bibliographic record
Abstract
Research shows that acoustic modifications in clearly enunciated fricative consonants (relative to the plain, conversational productions) facilitate auditory fricative 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 audio-visual perception of English sibilant and non-sibilant fricatives. Native English perceivers and non-native perceivers with different L1 fricative inventories (Mandarin, Korean) identified clear and plain fricative-vowel syllables in audio-only (AO), visual-only (VO), and audio-visual (AV) modes. The results across perceiver groups and speech styles showed an overall visual benefit and auditory dominance (AV>AO>VO). Comparisons of styles revealed clear-speech benefits in AO across fricatives and groups, but different patterns were noted in the visual conditions. In VO and AV, clear speech helped the more visually salient non-sibilant identification for native perceivers; however, clear-speech benefits were less prominent in non-natives’ perception of the non-sibilants, which are non-existent in their L1s. These findings are discussed in terms of the relative audio-visual weighting that benefits perception in clear speech as a function of input saliency and perceiver experience.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".