Weighting of cues to categorization of song versus speech in tone-language and non-tone-language speakers
Bibliographic record
Abstract
One of the most important auditory categorization tasks a listener faces is determining a sound's domain, a process which is a prerequisite for successful within-domain categorization tasks such as recognizing different speech sounds or musical tones. Speech and song are universal in human cultures: how do listeners categorize a sequence of words as belonging to one or the other of these domains? There is growing interest in the acoustic cues that distinguish speech and song, but it remains unclear whether there are cross-cultural differences in the evidence upon which listeners rely when making this fundamental perceptual categorization. Here we use the speech-to-song illusion, in which some spoken phrases perceptually transform into song when repeated, to investigate cues to this domain-level categorization in native speakers of tone languages (Mandarin and Cantonese speakers residing in the United Kingdom and China) and in native speakers of a non-tone language (English). We find that native tone-language and non-tone-language listeners largely agree on which spoken phrases sound like song after repetition, and we also find that the strength of this transformation is not significantly different across language backgrounds or countries of residence. Furthermore, we find a striking similarity in the cues upon which listeners rely when perceiving word sequences as singing versus speech, including small pitch intervals, flat within-syllable pitch contours, and steady beats. These findings support the view that there are certain widespread cross-cultural similarities in the mechanisms by which listeners judge if a word sequence is spoken or sung.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".