1. School of Chinese Language and Literature, Beijing Normal University, Beijing 100875, China;
2. Institute of Linguistics, Chinese Academy of Social Sciences, Beijing 100732, China
Abstract：Neural network models of child language acquisition are used to simulate children's phoneme acquisition for selected vowels and consonants of Standard German based on the growing self-organizing map (GSOM) modeling algorithm. An optimized growing strategy and a "cyclical reinforcing and reviewing training" procedure are integrated into the traditional GSOM algorithm.Simulations show that the "cyclical reinforcing and reviewing training" procedure significantly improves the learning quality of the network with the algorithm recognizing the vowel and manner of articulation categories to build the corresponding knowledge network. The modeling result reveals that during language acquisition, children have the ability to utilize acoustic features to acquire vowels and articulation categories, and to build acoustic space relations among different vowels.
曹梦雪, 李爱军, 方强. 基于GSOM模型的音位范畴习得建模[J]. 清华大学学报（自然科学版）, 2016, 56(11): 1154-1160.
CAO Mengxue, LI Aijun, FANG Qiang. GSOM-based modeling study of phoneme acquisition. Journal of Tsinghua University(Science and Technology), 2016, 56(11): 1154-1160.
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2016, Vol. 56 
