中华耳科学杂志（英文版）2006，Vol.1Issue(1)：30-34,5.
Mandarin Chinese Tone Recognition with an Artificial Neural Network
Mandarin Chinese Tone Recognition with an Artificial Neural Network
摘要
Abstract
Mandarin Chinese tone patterns vary in one of the four ways, i.e, (1) high level; (2) rising; (3) low falling and rising; and (4) high falling. The present study is to examine the efficacy of an artificial neural network in recognizing these tone patterns. Speech data were recorded from 12 children (3-6 years of age) and 15 adults. All subjects were native Mandarin Chinese speakers. The fundamental frequencies (FO) of each monosyllabic word of the speech data were extracted with an autocorrelation method. The pitch data(i.e., the FO contours) were the inputs to a feed-forward backpropagation artificial neural network. The number of inputs to the neural network varied from 1 to 16 and the hidden layer of the network contained neurons that varied from 1 to 16 in number. The output of the network consisted of four neurons representing the four tone patterns of Mandarin Chinese. After being trained with the Levenberg-Marquardt optimization, the neural network was able to successfully classify the tone patterns with an accuracy of about 90% correct for speech samples from both adults and children. The artificial neural network may provide an objective and effective way of assessing tone production in prelingually-deafened children who have received cochlear implants.关键词
tone recognition/artificial neural network/tone production/ChineseKey words
tone recognition/artificial neural network/tone production/Chinese分类
医药卫生引用本文复制引用
XU Li,ZHANG Wenle,ZHOU Ning,LEE Chaoyang,LI Yongxin,CHEN Xiuwu,ZHAO Xiaoyan..Mandarin Chinese Tone Recognition with an Artificial Neural Network[J].中华耳科学杂志（英文版）,2006,1(1):30-34,5.基金项目
We are grateful to Erin Furman, Huihui Li, Chessy Seebohm, Jessica Wolfanger, and Yunfang Zheng for their technical support. The study was supported in part by NIH NIDCD Grant R03-DC006161. （）
