Presentation	2009/12/14 Evaluation of Unsupervised Language Model Adaptation based on Topic-related Word Estimation using WWW Ryo Masumura, Masashi Ito, Akinori Ito, Shozo Makino,
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Abstract(in English)	To improve the accuracy of an LVCSR system, we gather topic-related documents from WWW, and adapt the language model. We focus on an unsupervised method that automatically generate search queries from an automatic transcription by a speech recognizer. In this paper, we proposed a new method to estimate topic-related word and sub-topic by extracting feature vectors from WWW, which express relevance between the words. We carried out a speech recognition experiment. The experimental result showed effectiveness of the proposed method.
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Committee	NLC
Conference Date	2009/12/14(1days)
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Registration To	Natural Language Understanding and Models of Communication (NLC)
Language	JPN
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Title (in English)	Evaluation of Unsupervised Language Model Adaptation based on Topic-related Word Estimation using WWW
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1st Author's Name	Ryo Masumura
1st Author's Affiliation	Graduate School of Engineering, Tohoku University()
2nd Author's Name	Masashi Ito
2nd Author's Affiliation	Graduate School of Engineering, Tohoku University
3rd Author's Name	Akinori Ito
3rd Author's Affiliation	Graduate School of Engineering, Tohoku University
4th Author's Name	Shozo Makino
4th Author's Affiliation	Graduate School of Engineering, Tohoku University
Date	2009/12/14
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Volume (vol)	vol.109
Number (no)	355
Page	pp.pp.-
#Pages	6
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