Perceived rhythmic regularity is greater for song than speech: examining acoustic correlates of rhythmic regularity in speech and song
Bibliographic record
Abstract
Rhythm is a key feature of music and language, but the way rhythm unfolds within each domain differs. Music induces perception of a beat, a regular repeating pulse spaced by roughly equal durations, whereas speech does not have the same isochronous framework. Although rhythmic regularity is a defining feature of music and language, it is difficult to derive acoustic indices of the differences in rhythmic regularity between domains. The current study examined whether participants could provide subjective ratings of rhythmic regularity for acoustically matched (syllable-, tempo-, and contour-matched) and acoustically unmatched (varying in tempo, syllable number, semantics, and contour) exemplars of speech and song. We used subjective ratings to index the presence or absence of an underlying beat and correlated ratings with stimulus features to identify acoustic metrics of regularity. Experiment 1 highlighted that ratings based on the term "rhythmic regularity" did not result in consistent definitions of regularity across participants, with opposite ratings for participants who adopted a beat-based definition (song greater than speech), a normal-prosody definition (speech greater than song), or an unclear definition (no difference). Experiment 2 defined rhythmic regularity as how easy it would be to tap or clap to the utterances. Participants rated song as easier to clap or tap to than speech for both acoustically matched and unmatched datasets. Subjective regularity ratings from Experiment 2 illustrated that stimuli with longer syllable durations and with less spectral flux were rated as more rhythmically regular across domains. Our findings demonstrate that rhythmic regularity distinguishes speech from song and several key acoustic features can be used to predict listeners' perception of rhythmic regularity within and across domains as well.
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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 | 0.008 |
| 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 | 0.001 |
| 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".