Developmental changes in the categorization of speech and song
Bibliographic record
Abstract
Music and language are two fundamental forms of human communication. Many studies examine the development of music- and language-specific knowledge, but few studies compare how listeners know they are listening to music or language. Although we readily differentiate these domains, how we distinguish music and language-and especially speech and song- is not obvious. In two studies, we asked how listeners categorize speech and song. Study 1 used online survey data to illustrate that 4- to 17-year-olds and adults have verbalizable distinctions for speech and song. At all ages, listeners described speech and song differences based on acoustic features, but compared with older children, 4- to 7-year-olds more often used volume to describe differences, suggesting that they are still learning to identify the features most useful for differentiating speech from song. Study 2 used a perceptual categorization task to demonstrate that 4-8-year-olds and adults readily categorize speech and song, but this ability improves with age especially for identifying song. Despite generally rating song as more speech-like, 4- and 6-year-olds rated ambiguous speech-song stimuli as more song-like than 8-year-olds and adults. Four acoustic features predicted song ratings: F0 instability, utterance duration, harmonicity, and spectral flux. However, 4- and 6-year-olds' song ratings were better predicted by F0 instability than by harmonicity and utterance duration. These studies characterize how children develop conceptual and perceptual understandings of speech and song and suggest that children under age 8 are still learning what features are important for categorizing utterances as speech or song. RESEARCH HIGHLIGHTS: Children and adults conceptually and perceptually categorize speech and song from age 4. Listeners use F0 instability, harmonicity, spectral flux, and utterance duration to determine whether vocal stimuli sound like song. Acoustic cue weighting changes with age, becoming adult-like at age 8 for perceptual categorization and at age 12 for conceptual differentiation. Young children are still learning to categorize speech and song, which leaves open the possibility that music- and language-specific skills are not so domain-specific.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".