Can statistical learning bootstrap early language acquisition? A modeling investigation

Abstract

Before they even produce their first word, infants start developing a language-specific perception, recognize the auditory form of frequent words, and develop a rudimentary knowledge of grammatical categories. A major question in language development is: what mechanisms are responsible for the effortless learning infants demonstrate? In-laboratory experiments have shown that young infants are exquisitely sensitive to fine-grained statistical regularities of their speech input. This has led researchers to propose statistical learning as the cornerstone mechanism of early language acquisition. While the statistical learning account has been influential, the extent to which it can explain early language acquisition is still controversial. Recent computational studies provide evidence in favour of the statistical learning hypothesis for sound learning, but can this result be extended to higher level linguistic categories? Here, we introduce STELA, a developmental and psycholinguistic-inspired computational model that simulates how infants might learn at multiple linguistic levels simultaneously based on statistical analysis of raw audio signals. Our algorithm uses only the raw input without any human annotation, and it is trained to predict future segments of speech based on past ones. It reproduces the pattern of parallel learning across sound and word levels reported in infants: it learns to discriminate sounds, recognizes the auditory form of words, and organizes sounds and words along linguistic dimensions. This suggests that statistical learning from raw speech is sufficient to bootstrap early language acquisition at the sound and word levels.