基于K-L散度的最大后验弧主导的混淆网络生成算法

为快速生成高质量混淆网络,该文提出一种最大后验弧主导的快速生成算法。它只需遍历一遍Lattice,具有线性时间复杂度。采用K-L散度(Kullback-Leibler Divergence,KLD)来度量弧标号之间的发音相似性,改善了混淆网络生成中弧对齐的准确性。实验结果显示,所提算法在生成速度上和Xue的快速算法是可比的,而生成质量更好。通过采用KLD作为弧标号相似性测度,生成混淆网络的质量得到了进一步提高。

K-L Divergence based Confusion Network Generation Algorithm Guided with Maximum Posteriori Arc

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.