Acoustic-phonetic consequences of clear speech in Cantonese vowels and lexical tones
Bibliographic record
Abstract
In adverse communicative contexts, speakers will modify their speech style to enhance intelligibility, which can involve a range of acoustic and articulatory adjustments. Prior work has focused on the influence of speaking style on spectral (e.g., vowel space expansion) and temporal distinctions (e.g., voice-onset time). Thus, this study investigated the extent to which such adjustments extend to the pitch dimension, namely lexical tone. Ten native Cantonese speakers were recorded producing five monosyllables with six Cantonese tones embedded in sentence contexts in both clear and conversational speaking styles. Acoustic analyses revealed that speakers produced two distinct styles, with longer vowel durations and greater vowel dispersion in clear relative to conversational speech. However, with regards to lexical tone production, the results indicated only a marginal re-adjustment of tones within the tone space as a result of clear speech. Specific contour tones, such as the low- and high-rising tones, saw increases in their F0 offset. However, mean F0, F0 range and tonal dispersion did not differ substantially between styles. These findings indicate that speakers maintain tonal stability across speaking styles, suggesting that, relative to segments, lexical tones may not be prioritized as phonological contrasts vital to enhance the overall intelligibility of the utterance.
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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 | 0.001 | 0.001 |
| 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".