Human vocalization corpus: recordings of infant-directed and adult-directed speech and song in 21 societies
Bibliographic record
Abstract
This repository contains a corpus of 1615 audio recordings of speech and song collected in 21 societies, first reported in Moser et al. (2020; bioRxiv) and later published in Hilton & Moser et al. (2022; Nature Human Behaviour). For assistance using any of this, contact Cody Moser (cmoser2@ucmerced.edu), Courtney Hilton (courtney.hilton@auckland.ac.nz), and Samuel Mehr (mehr@hey.com). Two versions of the audio are included: raw audio (`IDS-corpus-raw.zip`) and audio that was edited to prepare the recordings for automatic acoustic feature extraction (`IDS-corpus-edited.zip`). `IDS-textGrids.zip` contains annotation files from Praat's silence detection method, which were manually reviewed for accuracy. These files are used with the audio extraction scripts associated with the project (see code linked in paper) to build the edited audio files. `IDS-fieldsites.csv` contains some fieldsite-level metadata; additional metadata is in the Supplementary Information of the paper. In the two .zip archives, filenames have the format XXXYYZ.wav, where "XXX" is a fieldsite code, "YY" is a participant number, and "Z" is a vocalization type. Fieldsite codes are: MBE: Mbendjele BaYaka HAD: Hadza NYA: Nyangatom TOP: Toposa BEJ: Beijing JEN: Jenu Kurubas MEN: Mentawai Islanders KRA: Krakow LIM: Rural Poland TUR: Turku USD: San Diego TOR: Toronto VAN: Tannese Vanuatuans PNG: Enga WEL: Wellington ARA: Arawak TSI: Tsimane SPA: Sápara & Achuar QUE: Quechua ACO: Afrocolombians MES: Colombian Mestizos Participant numbers are padded integers, starting with 01, and are unique within fieldsites. Vocalization types are: A: infant-directed song B: infant-directed speech C: adult-directed song D: adult-directed speech In a few cases, participants vocalized in a different language than was expected, given the primary language of their fieldsite (e.g., when the participant was multilingual, or if they sang a song that contains multiple languages, as in The Beatles' "Michelle"). The file `IDS-unexpectedLanguages.csv` at https://github.com/themusiclab/infant-speech-song/blob/main/data/IDS-unexpectedLanguages.csv contains an inventory of these examples from the English-speaking fieldsites. This issue only affects a small minority of the recordings, as it was typically avoided by the researchers collecting the recordings.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.041 | 0.036 |
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".