Computational model trained on First 1,000 Days home recordings learned thousands of words
A learning model trained on daily home recordings of individual children found English phoneme units and acquired thousands of words, following each child's trajectory.
Researchers working with the First 1,000 Days (1kD) dataset reported in Science a computational model of language learning trained on the everyday speech heard by individual children. The dataset contains longitudinal, ultra-dense daily audiovisual recordings of children in their home environments. science.org
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Each child's recorded input was paired with a cognitively grounded learning agent. The agent was trained incrementally on that input without prior linguistic knowledge. According to the authors, it discovered speech units corresponding to the English phoneme inventory and acquired thousands of words, closely mirroring individual developmental trajectories.
Learning generalized across children while preserving individual differences in rate and timing. The authors also report that learning depended not only on linguistic input but also on rehearsal of past experiences at the end of each training day.
The authors conclude that everyday environments provide sufficient structure for language acquisition, and describe the approach as a unified mechanistic framework for studying development in real-world contexts. The work links naturalistic experience (“nurture”) with internal learning mechanisms (“nature”). science.org
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