%0 Journal Article
%T Clustering algorithm based on the MIBC decision-tree for CSR<br>基于MBIC的决策树聚类算法在连续语音识别中的应用
%A CHEN Guo-ping
%A DU Li-min
%A FU Yue-wen
%A WANG Jin-lin
%A <br>陈国平
%A 杜利民
%A 付跃文
%A 王劲林
%J 计算机应用
%D 2005
%I 
%X an algorithm based on Minimum Bayesian Information Criterion(MBIC) was proposed to help optimize the node-splitting degree in a decision tree.First,it was proved in theory that MBIC can find a good balance between the complexity of model parameters and the scale of the training sets.Then,a formula was proposed to describe MBIC decision tree splitting and stopping criterion.Finally,the experiment on Chinese all-syllable recognition shows that MBIC has much better adaptive ability to variable acoustic model parameters and training sets than the classical Maximum Likeihood Criterion method.
%K Continuous Speech Recognition(CSR)
%K clustering based on decision-tree
%K Minimum Bayesian Information Criterion(MBIC)
%K splitting and stopping criterion<br>连续语音识别
%K 决策树聚类
%K 最小贝叶斯信息准则
%K 分裂停止准则
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=831E194C147C78FAAFCC50BC7ADD1732&aid=51818CD873D60679&yid=2DD7160C83D0ACED&vid=C5154311167311FE&iid=59906B3B2830C2C5&sid=558ECF0AA61051C6&eid=2E5149EC64D500A9&journal_id=1001-9081&journal_name=计算机应用&referenced_num=0&reference_num=5