Combined models for topic spotting and topic-dependent Language Modeling

被引：4

作者：

Bigi, B ^{[1
]}

De Mori, R ^{[1
]}

El-Beze, M ^{[1
]}

Spriet, T ^{[1
]}

机构：

[1] Univ Avignon, LIA, CERI IUP, F-84911 Avignon 9, France

来源：

1997 IEEE WORKSHOP ON AUTOMATIC SPEECH RECOGNITION AND UNDERSTANDING, PROCEEDINGS | 1997年

关键词：

D O I：

10.1109/ASRU.1997.659133

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A new statistical method for Language Modeling and spoken document classification is proposed, It is based on a mixture of topic dependent probabilities, Each topic dependent probability is in turn a mixture of n-gram probabilities and the probability of Kullback-Lieber (ML) distances between keyword unigrams and distribution obtained from the content of a cache memory, Experimental result on topic classification using a corpus of 60 Mword from the French newspaper Le Monde show the excellent performance of the cache memory and its complementary role in providing different statistics for the decision process.

引用

页码：535 / 542

页数：8