%0 Journal Article
%T K-L Divergence based Confusion Network Generation Algorithm Guided with Maximum Posteriori Arc<br>基于K-L散度的最大后验弧主导的混淆网络生成算法
%A Wang Huan-liang Han Ji-qing Zheng Tie-ran Li Hai-feng
%A <br>王欢良
%A 韩纪庆
%A 郑铁然
%A 李海峰
%J 电子与信息学报
%D 2008
%I 
%X In order to accelerate generation of confusion network with high quality, a fast algorithm with linear time complexity is proposed in this paper. The proposed algorithm is guided with maximum posteriori arc and only traverses the lattice one pass. Kullback-Leibler Divergence (KLD) is used to measure the similarity between two arc’s labels, which can improve the accuracy of arc alignment in the process of generating confusion network. The experimental results show that the proposed algorithm is comparable with Xue’s fast algorithm at generation speed while the quality of confusion network is significantly improved. Further improvement of the quality can be obtained by using KLD as similarity measure of arc’s labels.
%K Speech recognition
%K Confusion network
%K Lattice
%K Confusion network generation
%K K-L divergence<br>语音识别
%K 混淆网络
%K Lattice
%K 混淆网络生成
%K K-L散度
%K 散度
%K 最大后验
%K 网络生成
%K 快速生成算法
%K Maximum
%K Generation
%K Algorithm
%K Network
%K based
%K 相似性测度
%K 可比
%K 快速算法
%K 生成速度
%K 显示
%K 结果
%K 实验
%K 对齐
%K 改善
%K 发音
%K 标号
%K 度量
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=1319827C0C74AAE8D654BEA21B7F54D3&jid=EFC0377B03BD8D0EF4BBB548AC5F739A&aid=FB6788B9C0D26D504370A5619DA4B776&yid=67289AFF6305E306&vid=340AC2BF8E7AB4FD&iid=94C357A881DFC066&sid=8DABBEB130EFF191&eid=1AA557EFF1C6B447&journal_id=1009-5896&journal_name=电子与信息学报&referenced_num=0&reference_num=10