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。

基于真实规模的视听命名事件的早期词汇习得模型

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

Knowledge in the human mind exhibits a dualistic vector/network nature.
Modelling words as vectors is key to natural language processing, whereas
networks of word associations can map the nature of semantic memory. We
reconcile these paradigms - fragmented across linguistics, psychology and
computer science - by introducing FEature-Rich MUltiplex LEXical (FERMULEX)
networks. This novel framework merges structural similarities in networks and
vector features of words, which can be combined or explored independently.
Similarities model heterogenous word associations across
semantic/syntactic/phonological aspects of knowledge. Words are enriched with
multi-dimensional feature embeddings including frequency, age of acquisition,
length and polysemy. These aspects enable unprecedented explorations of
cognitive knowledge. Through CHILDES data, we use FERMULEX networks to model
normative language acquisition by 1000 toddlers between 18 and 30 months.
Similarities and embeddings capture word homophily via conformity, which
measures assortative mixing via distance and features. Conformity unearths a
language kernel of frequent/polysemous/short nouns and verbs key for basic
sentence production, supporting recent evidence of children's syntactic
constructs emerging at 30 months. This kernel is invisible to network
core-detection and feature-only clustering: It emerges from the dual
vector/network nature of words. Our quantitative analysis reveals two key
strategies in early word learning. Modelling word acquisition as random walks
on FERMULEX topology, we highlight non-uniform filling of communicative
developmental inventories (CDIs). Conformity-based walkers lead to accurate
(75%), precise (55%) and partially well-recalled (34%) predictions of early
word learning in CDIs, providing quantitative support to previous empirical
findings and developmental theories.

本研究提出一种新的 FERMULEX 结构，通过多维度特征嵌入来丰富词义。我们运用该网络结构，研究了 1000 位 18-30 个月大的幼儿语言习得中的词汇选择偏好和语法建构，并发现其非均匀和规律性的演化过程。研究结果支持了以往的实证研究和发展性理论。