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.

在一个真实的环境中，使用一种仅通过未注释的原始语音和视觉输入中的统计规律性进行学习的模型，研究证明婴儿通过统计学习能够有效地学习识别单词并将其与相应的视觉对象相关联，词汇增长的速度与婴儿观察到的相当。这一发现支持了统计学习在早期词知觉中的可行性，展示了学习可以在不假设任何先前语言能力的情况下进行。