Learning to understand speech appears almost effortless for typically developing infants, yet from an information-processing perspective, acquiring a language from acoustic speech is an enormous challenge. This chapter reviews recent developments in using computational models to understand early language acquisition from speech and audiovisual input. The focus is on self-supervised and visually grounded models of perceptual learning. We show how these models are becoming increasingly powerful in learning various aspects of speech without strong linguistic priors, and how many features of early language development can be explained through a shared set of learning principles-principles broadly compatible with multiple theories of language acquisition and human cognition. We also discuss how modern learning simulations are gradually becoming more realistic, both in terms of input data and in linking model behavior to empirical findings on infant language development.
无语言先验条件下从声学语音及视听输入进行早期语言学习的计算建模 / Computational modeling of early language learning from acoustic speech and audiovisual input without linguistic priors
这篇论文通过回顾自监督和视觉基础的计算模型，阐述了如何在没有强语言先验知识的情况下，利用这些模型从语音和视听输入中学习语言的各个方面，并揭示了这些模型如何以一套共享的学习原则来解释早期语言发展的许多特征，同时与现代婴儿语言发展的实证研究结果逐渐接轨。
源自 arXiv: 2603.08359
