Discriminative Named Entity Recognition of Speech Data using Speech Recognition Confidence

被引：0
|
作者：
Sudoh, Katsuhito [1 ]
Tsukada, Hajime [1 ]
Isozaki, Hideki [1 ]
机构：
[1] NTT, Commun Sci Labs, Keihanna Sci City, Kyoto 6190237, Japan
来源：
INTERSPEECH 2006 AND 9TH INTERNATIONAL CONFERENCE ON SPOKEN LANGUAGE PROCESSING, VOLS 1-5 | 2006年
关键词：
named entity recognition; speech recognition; confidence scoring; discriminative models; information retrieval;
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 presents a method for the named entity recognition (NER) of speech data that uses automatic speech recognition (ASR) confidence as a feature that indicates whether each word is correctly recognized. An NER model is trained using ASR results with named entity (NE) labels to include an ASR confidence feature as well as corresponding transcriptions with NE labels. Experiments using support vector machines (SVMs) and speech data from Japanese newspaper articles show that the proposed method achieves higher F-measure in NER than a simple application of text-based NER to ASR results.
引用
收藏
页码：337 / 340
页数：4
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