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Syllable Learning – Based on Hebbian Cell Assemblies and Reinforcement Learning

By Sepideh Fazeli and Fariba Bahrami

Abstract

Abstract—Investigating language acquisition is one of the most challenging problems in the area of studying language. Syllable learning as a level of language acquisition has a considerable significance since it plays an important role in language acquisition. Because of impossibility of studying language acquisition directly with children, especially in its developmental phases, computer models will be useful in examining language acquisition. In this paper a computer model of early language learning for syllable learning is proposed. It is guided by a conceptual model of syllable learning which is named Directions Into Velocities of Articulators model (DIVA). The computer model uses simple associational and reinforcement learning rules within neural network architecture which are inspired by neuroscience. Our simulation results verify the ability of the proposed computer model in producing phonemes during babbling and early speech. Also, it provides a framework for examining the neural basis of language learning and communication disorders. Keywords—Brain modeling, computer models, language acquisition, reinforcement learning

Topics: E
Year: 2013
OAI identifier: oai:CiteSeerX.psu:10.1.1.309.4441
Provided by: CiteSeerX
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