Incremental language modeling for broadcast news

Author

Ohtsuki, Katsutoshi ; Nguyen, Long

Author_Institution

NTT Cyber Space Lab., NTT Corp., Kanagawa

fYear

2005

fDate

27-27 Nov. 2005

Firstpage

139

Lastpage

144

Abstract

In this paper, we address the task of incremental language modeling for automatic transcription of broadcast news speech. Daily broadcast news naturally contains new words that are not in the lexicon of the speech recognition system but are important for downstream applications such as information retrieval or machine translation. To recognize those new words, the lexicon and the language model of the speech recognition system need to be updated periodically. We propose a method of estimating a list of words to be added to the lexicon based on some time-series text data. The experimental results on the RT04 broadcast news data and other TV audio data showed that this method provided a decent and stable reduction in both out-of-vocabulary rates and speech recognition word error rates

Keywords

information retrieval; language translation; speech recognition; automatic transcription; broadcast news speech; incremental language modeling; information retrieval; machine translation; speech recognition system; speech recognition word error rates; Broadcasting; Error analysis; Frequency; Information retrieval; Laboratories; Natural languages; Space technology; Speech recognition; Vocabulary; World Wide Web;

fLanguage

English

Publisher

ieee

Conference_Titel

Automatic Speech Recognition and Understanding, 2005 IEEE Workshop on

Conference_Location

San Juan

Print_ISBN

0-7803-9478-X

Electronic_ISBN

0-7803-9479-8

Type

conf

DOI

10.1109/ASRU.2005.1566531

Filename

1566531