CLASS-COMBINED ORD N-GRAM FOR ROBUST LANGUAGE ODELING

Norihiko Kobayashi; Tetsunori Kobayashi

CLASS-COMBINED ORD N-GRAM FOR ROBUST LANGUAGE ODELING

Norihiko Kobayashi, Tetsunori Kobayashi

基幹理工学部

研究成果: Paper › 査読

1 被引用数 (Scopus)

抄録

We propose a method of robust language model ing for a small amount of training text corpus. In this method, the word bigram and the class bigram are combined using a weighting function of preceding word frequency. We made experiments on speech recogni tion using JNAS speech corpus. As the results, it was proved that the performance of the class combined bi gram is equivalent to that of the word bigram trained with 2.5 larger size of corpus. We also made experi ments using sports news dialogue on TV. Recognition accuracy of the class-combined bigram was 83.3% that was 5.5 point higher than that of the word bigram.

本文言語	English
ページ	1599-1602
ページ数	4
出版ステータス	Published - 1999
イベント	6th European Conference on Speech Communication and Technology, EUROSPEECH 1999 - Budapest, Hungary 継続期間: 1999 9月 5 → 1999 9月 9

Conference

Conference	6th European Conference on Speech Communication and Technology, EUROSPEECH 1999
国/地域	Hungary
City	Budapest
Period	99/9/5 → 99/9/9

ASJC Scopus subject areas

コンピュータサイエンスの応用
ソフトウェア
言語学および言語
通信

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引用スタイル

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author = "Norihiko Kobayashi and Tetsunori Kobayashi",

note = "Publisher Copyright: {\textcopyright} 1999 6th European Conference on Speech Communication and Technology, EUROSPEECH 1999. All rights reserved.; 6th European Conference on Speech Communication and Technology, EUROSPEECH 1999 ; Conference date: 05-09-1999 Through 09-09-1999",

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AU - Kobayashi, Tetsunori

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AB - We propose a method of robust language model ing for a small amount of training text corpus. In this method, the word bigram and the class bigram are combined using a weighting function of preceding word frequency. We made experiments on speech recogni tion using JNAS speech corpus. As the results, it was proved that the performance of the class combined bi gram is equivalent to that of the word bigram trained with 2.5 larger size of corpus. We also made experi ments using sports news dialogue on TV. Recognition accuracy of the class-combined bigram was 83.3% that was 5.5 point higher than that of the word bigram.

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CLASS-COMBINED ORD N-GRAM FOR ROBUST LANGUAGE ODELING

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Conference

ASJC Scopus subject areas

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