Automatized analysis of children's exposure to child-directed speech in reschool settings: Validation and application.

ABSTRACT: The present study explored whether a tool for automatic detection and recognition of interactions and child-directed speech (CDS) in preschool classrooms could be developed, validated, and applied to non-coded video recordings representing children's classroom experiences. Using first-person video recordings collected by 13 preschool children during a morning in their classrooms, we extracted high-level audiovisual features from recordings using automatic speech recognition and computer vision services from a cloud computing provider. Using manual coding for interactions and transcriptions of CDS as reference, we trained and tested supervised classifiers and linear mappings to measure five variables of interest. We show that the supervised classifiers trained with speech activity, proximit

SUBMITTER: Gonzalez Villasanti H 

PROVIDER: S-EPMC7688182 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature