Rhythm in speech and animal vocalizations: a cross‐species perspective
Bibliographic record
Abstract
Why does human speech have rhythm? As we cannot travel back in time to witness how speech developed its rhythmic properties and why humans have the cognitive skills to process them, we rely on alternative methods to find out. One powerful tool is the comparative approach: studying the presence or absence of cognitive/behavioral traits in other species to determine which traits are shared between species and which are recent human inventions. Vocalizations of many species exhibit temporal structure, but little is known about how these rhythmic structures evolved, are perceived and produced, their biological and developmental bases, and communicative functions. We review the literature on rhythm in speech and animal vocalizations as a first step toward understanding similarities and differences across species. We extend this review to quantitative techniques that are useful for computing rhythmic structure in acoustic sequences and hence facilitate cross-species research. We report links between vocal perception and motor coordination and the differentiation of rhythm based on hierarchical temporal structure. While still far from a complete cross-species perspective of speech rhythm, our review puts some pieces of the puzzle together.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".