Combined models for topic spotting and topic-dependent language modeling

Author

Bigi, Brigitte ; Mori, Renato Dee ; El-Béze, Marc ; Spriet, Thierry

Author_Institution

Avignon Univ., France

fYear

1997

fDate

14-17 Dec 1997

Firstpage

535

Lastpage

542

Abstract

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 (KL) 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

Keywords

cache storage; natural languages; pattern classification; probability; speech recognition; French newspaper Le Monde; Kullback-Lieber distances; cache memory; combined models; decision process; keyword unigrams; n-gram probabilities; spoken document classification; statistical method; topic classification; topic dependent language modeling; topic dependent probabilities; topic spotting; Cache memory; History; Information retrieval; Natural languages; Probability; Statistical analysis; Statistical distributions; Vocabulary;

fLanguage

English

Publisher

ieee

Conference_Titel

Automatic Speech Recognition and Understanding, 1997. Proceedings., 1997 IEEE Workshop on

Conference_Location

Santa Barbara, CA

Print_ISBN

0-7803-3698-4

Type

conf

DOI

10.1109/ASRU.1997.659133

Filename

659133