English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

A systematic review and Bayesian meta-analysis of the acoustic features of infant-directed speech

MPS-Authors
/persons/resource/persons41950

Bergmann,  Christina
Language Development Department, MPI for Psycholinguistics, Max Planck Society;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
Supplementary Material (public)

Cox_etal_2022suppl_systematic review and....pdf
(Supplementary material), 7MB

Citation

Cox, C., Bergmann, C., Fowler, E., Keren-Portnoy, T., Roepstorff, A., Bryant, G., et al. (2023). A systematic review and Bayesian meta-analysis of the acoustic features of infant-directed speech. Nature Human Behaviour, 7, 114-133. doi:10.1038/s41562-022-01452-1.


Cite as: https://hdl.handle.net/21.11116/0000-000A-E6AC-1
Abstract
When speaking to infants, adults often produce speech that differs systematically from that directed to other adults. In order to quantify the acoustic properties of this speech style across a wide variety of languages and cultures, we extracted results from empirical studies on the acoustic features of infant-directed speech (IDS). We analyzed data from 88 unique studies (734 effect sizes) on the following five acoustic parameters that have been systematically examined in the literature: i) fundamental frequency (fo), ii) fo variability, iii) vowel space area, iv) articulation rate, and v) vowel duration. Moderator analyses were conducted in hierarchical Bayesian robust regression models in order to examine how these features change with infant age and differ across languages, experimental tasks and recording environments. The moderator analyses indicated that fo, articulation rate, and vowel duration became more similar to adult-directed speech (ADS) over time, whereas fo variability and vowel space area exhibited stability throughout development. These results point the way for future research to disentangle different accounts of the functions and learnability of IDS by conducting theory-driven comparisons among different languages and using computational models to formulate testable predictions.