Multi-modal cross-linguistic perception of Mandarin tones in clear speech
Bibliographic record
Abstract
Clearly enunciated speech (relative to conversational, plain speech) involves articulatory and acoustic modifications that enhance auditory-visual (AV) 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. Since tone production does not primarily rely on vocal tract configurations, tones may be less visually distinctive. Questions thus arise as to whether clear speech can enhance visual tone intelligibility, and if so, whether any intelligibility gain can be attributable to tone-specific category-enhancing (code-based) clear-speech cues or tone-general saliency-enhancing (signal-based) cues. The present study addresses these questions by examining the identification of clear and plain Mandarin tones with visual-only, auditory-only, and AV input modalities by native (Mandarin) and nonnative (English) perceivers. Results show that code-based visual and acoustic clear tone modifications, although limited, affect both native and nonnative intelligibility, with category-enhancing cues increasing intelligibility and category-blurring cues decreasing intelligibility. In contrast, signal-based cues, which are extensively available, do not benefit native intelligibility, although they contribute to nonnative intelligibility gain. These findings demonstrate that linguistically relevant visual tonal cues are existent. In clear speech, such tone category-enhancing cues are incorporated with saliency-enhancing cues across AV modalities for intelligibility improvements.
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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) | 0.002 | 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 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".