Acoustic foundations of the speech-to-song illusion.
Bibliographic record
Abstract
In the "speech-to-song illusion," certain spoken phrases are heard as highly song-like when isolated from context and repeated. This phenomenon occurs to a greater degree for some stimuli than for others, suggesting that particular cues prompt listeners to perceive a spoken phrase as song. Here we investigated the nature of these cues across four experiments. In Experiment 1, participants were asked to rate how song-like spoken phrases were after each of eight repetitions. Initial ratings were correlated with the consistency of an underlying beat and within-syllable pitch slope, while rating change was linked to beat consistency, within-syllable pitch slope, and melodic structure. In Experiment 2, the within-syllable pitch slope of the stimuli was manipulated, and this manipulation changed the extent to which participants heard certain stimuli as more musical than others. In Experiment 3, the extent to which the pitch sequences of a phrase fit a computational model of melodic structure was altered, but this manipulation did not have a significant effect on musicality ratings. In Experiment 4, the consistency of intersyllable timing was manipulated, but this manipulation did not have an effect on the change in perceived musicality after repetition. Our methods provide a new way of studying the causal role of specific acoustic features in the speech-to-song illusion via subtle acoustic manipulations of speech, and show that listeners can rapidly (and implicitly) assess the degree to which nonmusical stimuli contain musical structure. (PsycINFO Database Record
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".