Training a language model using webdata for large vocabulary Japanese spontaneous speech recognition

被引：0
|
作者：
Masumura, Ryo [1 ]
Hahm, Seongjun [1 ]
Ito, Akinori [1 ]
机构：
[1] Tohoku Univ, Grad Sch Engn, Sendai, Miyagi 980, Japan
来源：
12TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2011 (INTERSPEECH 2011), VOLS 1-5 | 2011年
关键词：
Spontaneous speech recognition; language model; World Wide Web; large vocabulary continuous speech recognition; Corpus of Spontaneous Japanese;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper describes a language modeling method using large-scale spoken language data retrieved from the Web for spontaneous speech recognition. We downloaded 15 million Web pages on a comprehensive range topics. Next, spoken language-like texts were selected from the downloaded Web data using the naive Bayes classifier, and typical linguistic phenomena such as fillers and pauses were added using simulation models. A language model trained by the generated data gave as high performance as the large-scale spontaneous speech corpus (Corpus of Spontaneous Japanese, CSJ). By combining the generated data and CSJ, we improved word accuracy.
引用
收藏
页码：1476 / 1479
页数：4
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