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Journal of Information Science and Engineering, Vol. 27 No. 5, pp. 1613-1639 (September 2011)

Towards Understanding Child Language Acquisition: An Unsupervised Multimodal Neural Network Approach*

ABEL NYAMAPFENE
College of Engineering, Mathematics and Physical Sciences
University of Exeter
Exeter, EX4 4QF, UK

This paper presents an unsupervised, multimodal, neural network model of early child language acquisition that takes into account the childs communicative intentions as well as the multimodal nature of language. The model exhibits aspects of one-word child language such as generalisation to new and unforeseen utterances, a U-shaped learning trajectory and a vocabulary spurt. A probabilistic gating mechanism that predisposes the model to utter single words at the onset of training and two-words as training progresses enables the model to exhibit the gradual and continuous transition between the one-word and two-word stages as observed in children.

Keywords: child language acquisition, one-word child language stage, two-word child language stage, unsupervised multimodal neural network, neural multinet, Hypermap

Full Text () Retrieve PDF document (201109_07.pdf)

Received December 23, 2009; revised March 15, 2010; accepted May 6, 2010.
Communicated by Chin-Teng Lin.