基于GSOM模型的音位范畴习得建模  

GSOM-based modeling study of phoneme acquisition

作　　者：曹梦雪[1] 李爱军[2] 方强[2] 

机构地区：[1]北京师范大学文学院,北京100875 [2]中国社会科学院语言研究所,北京100732

出　　处：《清华大学学报（自然科学版）》2016年第11期1154-1160,共7页Journal of Tsinghua University(Science and Technology)

基　　金：中国博士后科学基金面上资助项目(2016M590057);北京师范大学青年教师基金资助项目;国家自然科学基金重点项目(61233009);中国社会科学院哲学社会科学创新工程“语音与言语科学重点实验室”项目

摘　　要：为了探究神经网络模型在儿童语言习得模拟研究中的应用,该文在可扩展的自组织网络模型（growing selforganizing map,GSOM）算法的基础上,模拟了婴幼儿习得标准德语部分元音和辅音音位范畴的过程。该研究将优化的网络扩展策略和“循环性强化和复习训练”学习算法与传统的GSOM算法进行了结合。模拟结果显示：“循环性强化和复习训练”算法可以有效地提高模型网络的学习质量;模型算法可以较好地习得元音音位和辅音发音方式的范畴,并构建相应的知识网络。建模研究的结果表明：在习得语言的过程中,通过对语音声学信息的加工,婴幼儿有能力习得元音音位和辅音发音方式的范畴,并构建元音音位在声学空间内的分布关系。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.

关 键 词：神经计算模型 可扩展的自组织网络 儿童语言习得 范畴化学习 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]