摘要
本文研究了音字转换中使用的语言模型问题,从语言模型复杂度的角度研究了三种解决零概率问题的方法的效率,back-off方法,删除插值和非线性插值方法.并且给出了这些方法下使得语言模型复杂度最小的参数的递推公式,通过实验验证了这些递推公式的正确性.
This paper deals with the problem of langUage model in translation of Chinese spelling to characters. From the point of perplexity of langUage model, this paper discusses the efficiency of three kinds of methods on probabilities estimation of sparse data, which are back-off, deleted interpolation and nonlinear interpolation. Moreover, an iterative formula of Parameters to get the minimum perplexity of langUage model under the three methods is proud and proved by experiments.
出处
《电子学报》
EI
CAS
CSCD
北大核心
1998年第8期43-46,共4页
Acta Electronica Sinica
关键词
语音识别
语言模型
N元文法
Speech recognition, Language model, N-gram model