The timing of speech and gesture in two Niger-Congo languages: Implications for word-level prominence
Bibliographic record
Abstract
Co-speech gestures are timed to occur with prosodically prominent syllables in several languages. In prior work in Indo-European languages, gestures are found to be attracted to stressed syllables, with gesture apexes preferentially aligning with syllables bearing higher and more dynamic pitch accents. Little research has examined the temporal alignment of co-speech gestures in African tonal languages, where metrical prominence is often hard to identify due to a lack of canonical stress correlates, and where a key function of pitch is in distinguishing between words, rather than marking intonational prominence. Here, we examine the alignment of co-speech gestures in two different Niger-Congo languages with very different word structures, Medʉmba (Grassfields Bantu, Cameroon) and Igbo (Igboid, Nigeria). Our findings suggest that the initial position in the stem tends to attract gestures in Medʉmba, while the final syllable in the word is the default position for gesture alignment in Igbo; phrase position also influences gesture alignment, but in language-specific ways. Though neither language showed strong evidence of elevated prominence of any individual tone value, gesture patterning in Igbo suggests that metrical structure at the level of the tonal foot is relevant to the speech-gesture relationship. Our results demonstrate how the speech-gesture relationship can be a window into patterns of word- and phrase-level prosody cross-linguistically. They also show that the relationship between gesture and tone (and the related notion of ‘tonal prominence’) is mediated by tone’s function in a language.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".