Audio-visual perception of mandarin tone in clear speech
Bibliographic record
Abstract
Clearly enunciated speech (relative to conversational, plain speech) involves articulatory and acoustic modifications that have been shown to enhance segmental intelligibility. However, little research has explored clear-speech effects on the perception of suprasegmental properties such as lexical tone, particularly involving visual (facial) perception. Moreover, research has not determined if visual tonal cues are linguistically significant, presumably because tone production does not rely on vocal tract configurations. Investigating clear-speech effects may help validate the contribution of visual information, since improved visual tone intelligibility in clear speech would indicate the linguistic relevance of visual cues. The present study tested this hypothesis by examining the intelligibility of clear and plain Mandarin tones by native (Mandarin) and non-native (English) perceivers with auditory/visual input modalities (AO: audio-only; VO: visual-only; and AV: audio-visual). Results showed small but significant clear-speech benefits in each modality for both natives and non-natives. However, while the natives revealed an overall visual gain and auditory dominance (AV>AO>VO), visual gain appeared to be less prominent in non-native perception (AV VO in plain, AV = AO>VO in clear). These results demonstrate clear-speech facilitation in visual as well as auditory perception, particularly with native perceivers, suggesting the existence of linguistically relevant visual tonal cues.
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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 | 0.000 | 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".