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, proximity, and high-level facial features achieve adequate accuracy in detecting interactions. Furthermore, in combination with an automatic speech recognition service, the supervised classifier achieved error rates for CDS measures that are in line with other open-source automatic decoding tools in early childhood settings. Finally, we demonstrate our tool's applicability by using it to automatically code and transcribe children's interactions and CDS exposure vertically within a classroom day (morning to afternoon) and horizontally over time (fall to winter). Developing and scaling tools for automatized capture of children's interactions with others in the preschool classroom, as well as exposure to CDS, may revolutionize scientific efforts to identify precise mechanisms that foster young children's language development.
Highlights
Language development during the early years of life is driven largely by exposure to the talk of others [1], and linguistic input directed to the child serves to shape the cortical regions of the brain responsible for processing linguistic forms and functions [2]
The purpose of the present work is to present the Classroom Interaction Detection and Recognition (CIDR) system for the automatic transcription and coding of child-directed speech (CDS) experienced by children in preschool settings, empirically validate the CIDR system, and apply the system to a novel corpus of observational data
The performance of the unsupervised reduced features bi-directional LSTM (BILSTM) and full BILSTM detectors were compared with the reference RI obtained from manual coding
Summary
Language development during the early years of life is driven largely by exposure to the talk of others [1], and linguistic input directed to the child serves to shape the cortical regions of the brain responsible for processing linguistic forms and functions [2]. While there is some controversy as to whether such linguistic input must be directed explicitly to the child, versus overhead by the child but directed to others [3, 4], compelling evidence indicates that child-. Automatized analysis of children’s exposure to child-directed speech in preschool settings m54pc/?view_only= 1ad89d9d285c4cb9aa32141ca10739d5
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