%0 Journal Article
%T Center-distance continuous probability models and the distance measure<br>Center-Distance Continuous Probability Models and the Distance Measure
%A Zheng Fang
%A Wu Wenhu
%A Fang Ditang
%A <br>Zheng Fang
%A Wu Wenhu
%A Fang Ditang
%J 计算机科学技术学报
%D 1998
%I 
%X In this paper, a new statistic model named Center-Distance Continuous Probability Model (CDCPM) for speech recognition is described, which is based on Center-Distance Normal (CDN) distribution. In a CDCPM, the probability transition matrix is omitted, and the observation probability density function (PDF) in each state is in the form of embedded multiple-model (EMM) based on the Nearest Neighbour rule. The experimental results on two giant real-world Chinese speech databases and a real-world continuous-manner 2000 phrase system show that this model is a powerful one. Also,a distance measure for CDCPMs is proposed which is based on the Bayesian minimum classification error (MCE) discrimination.
%K Center-distance continuous probability model (CDCPM)
%K center-distance normal (CDN) distribution
%K embedded multiple-model (EMM) scheme
%K minimum classification error (MCE)<br>语言识别
%K 几率模型
%K 距离测试
%K 信息处理
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=F57FEF5FAEE544283F43708D560ABF1B&aid=823F7DD8296030680A274030570A59E2&yid=8CAA3A429E3EA654&vid=FC0714F8D2EB605D&iid=94C357A881DFC066&sid=BA48F0B914ED890A&eid=B40AD8FE6FA88DE9&journal_id=1000-9000&journal_name=计算机科学技术学报&referenced_num=3&reference_num=24