Multiparty speech predominates in children's language environments across diverse cultural settings
Year: 2026
Type: dataset
Abstract: This dataset reports on 844 children's participation in conversation across 10 countries, based on automated speech metrics derived from 23,000 hours of daylong naturalistic audio recordings. The main analysis focused on quantifying children exposure to different types of conversation, namely: multiparty conversation (2+ interlocutors), adult-child conversation (1 adult interlocutor), child-child conversation (1 other child interlocutor), and overheard conversation (no target child participation). Potential sources of variance in exposure to conversation, including child age, gender, number of siblings, and maternal education, are also examined across countries and individual homes. This resource exists to archive supplementary materials and facilitate the reproduction of the full manuscript, which is currently under review. Submodules in the datasets and input directories exist to preserve dataset structure; additional permission must be sought from the authors for access, detailed in the README.
Source: German Neuroinformatics Node
Institutions: Centre National de la Recherche Scientifique, Université Paris Sciences et Lettres, École Normale Supérieure - PSL, Laboratoire de Sciences Cognitives et Psycholinguistique, École des hautes études en sciences sociales, Institute of Psychology, Polish Academy of Sciences, University of Zurich, University of Haifa, Stanford University, Washington State University, University of California, Los Angeles
Language: English
Subfield: Developmental and Educational Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: green
Awards: RIDIR: Collaborative Research: Enabling Access to and Analysis of Shared Daylong Child and Family Audio Data, CompCog: Infant Vocalization as Foraging for Caregiver Responses, RIDIR: Collaborative Research: Enabling Access to and Analysis of Shared Daylong Child and Family Audio Data, G9000061138, G9000332580
OpenAlex ID: W7206153443
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