title: Interconnected growing self-organizing maps for auditory and semantic acquisition modeling.
abstract: Based on the incremental nature of knowledge acquisition, in this study we propose a growing self-organizing neural network approach for modeling the acquisition of auditory and semantic categories. We introduce an Interconnected Growing Self-Organizing Maps (I-GSOM) algorithm, which takes associations between auditory information and semantic information into consideration, in this paper. Direct phonetic-semantic association is simulated in order to model the language acquisition in early phases, such as the babbling and imitation stages, in which no phonological representations exist. Based on the I-GSOM algorithm, we conducted experiments using paired acoustic and semantic training data. We use a cyclical reinforcing and reviewing training procedure to model the teaching and learning process between children and their communication partners. A reinforcing-by-link training procedure and a link-forgetting procedure are introduced to model the acquisition of associative relations between auditory and semantic information. Experimental results indicate that (1) I-GSOM has good ability to learn auditory and semantic categories presented within the training data; (2) clear auditory and semantic boundaries can be found in the network representation; (3) cyclical reinforcing and reviewing training leads to a detailed categorization as well as to a detailed clustering, while keeping the clusters that have already been learned and the network structure that has already been developed stable; and (4) reinforcing-by-link training leads to well-perceived auditory-semantic associations. Our I-GSOM model suggests that it is important to associate auditory information with semantic information during language acquisition. Despite its high level of abstraction, our I-GSOM approach can be interpreted as a biologically-inspired neurocomputational model.
authors.affiliation: Laboratory of Phonetics and Speech Science, Institute of Linguistics, Chinese Academy of Social Sciences Beijing, China.
authors.forename: Mengxue
authors.initials: M
authors.lastname: Cao
authors.affiliation: Laboratory of Phonetics and Speech Science, Institute of Linguistics, Chinese Academy of Social Sciences Beijing, China.
authors.forename: Aijun
authors.initials: A
authors.lastname: Li
authors.affiliation: Laboratory of Phonetics and Speech Science, Institute of Linguistics, Chinese Academy of Social Sciences Beijing, China.
authors.forename: Qiang
authors.initials: Q
authors.lastname: Fang
authors.affiliation: Department of Special Education, Faculty of Human Sciences, University of Cologne Cologne, Germany.
authors.forename: Emily
authors.initials: E
authors.lastname: Kaufmann
authors.affiliation: Neurophonetics Group, Department of Phoniatrics, Pedaudiology, and Communication Disorders, Medical School, RWTH Aachen University Aachen, Germany ; Cognitive Computation and Applications Laboratory, School of Computer Science and Technology, Tianjin University Tianjin, China.
authors.forename: Bernd J
authors.initials: BJ
authors.lastname: Kröger
keywords: auditory feature map; auditory–semantic association; interconnected growing self-organizing map; language acquisition; neural network; neurocomputational models; semantic feature map