A model of early word acquisition based on realistic-scale audiovisual naming events

Infants gradually learn to parse continuous speech into words and connect names with objects, yet the mechanisms behind development of early word perception skills remain unknown. We studied the extent to which early words can be acquired through statistical learning from regularities in audiovisual sensory input. We simulated word learning in infants up to 12 months of age in a realistic setting, using a model that solely learns from statistical regularities in unannotated raw speech and pixel-level visual input. Crucially, the quantity of object naming events was carefully designed to match that accessible to infants of comparable ages. Results show that the model effectively learns to recognize words and associate them with corresponding visual objects, with a vocabulary growth rate comparable to that observed in infants. The findings support the viability of general statistical learning for early word perception, demonstrating how learning can operate without assuming any prior linguistic capabilities. • A model of cross-situational audiovisual word acquisition in infants aged 0–12 months. • Learning simulated with realistic-scale speech and audiovisual naming events. • The learner model succeeds in learning words and word meanings from the input. • Feasibility proof for generic statistical learning in early language bootstrapping.