LLMs Special Issue

Modeling the initial state of early phonetic learning in infants

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Abstract

What are the necessary conditions to acquire language? Do infants rely on simple statistical mechanisms, or do they come pre-wired with innate capabilities allowing them to learn their native language(s)? Previous modeling studies have shown that unsupervised learning algorithms could reproduce some aspects of infant phonetic learning. Despite these successes, algorithms still fail to reproduce the learning trajectories observed in infants. Here, we advocate that this failure is partly due to a wrong initial state. Contrary to infants, unsupervised learning algorithms start with little to no prior knowledge of speech sounds. In this work, we propose a modeling approach to investigate the relative contribution of innate factors and language experience in infant speech perception. Our approach allows us to investigate theories hypothesizing a more significant role of innate factors, offering new modeling opportunities for studying infant language acquisition.

Keywords: phonetic learning, language acquisition, deep learning