Article | Proceedings of the 20th Nordic Conference of Computational Linguistics, NODALIDA 2015, May 11-13, 2015, Vilnius, Lithuania | Using sub-word n-gram models for dealing with OOV in large vocabulary speech recognition for Latvian Link�ping University Electronic Press Conference Proceedings
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Title:
Using sub-word n-gram models for dealing with OOV in large vocabulary speech recognition for Latvian
Author:
Askars Salimbajevs: Tilde, Riga, Latvia Jevgenijs Strigins: Tilde, Riga, Latvia
Download:
Full text (pdf)
Year:
2015
Conference:
Proceedings of the 20th Nordic Conference of Computational Linguistics, NODALIDA 2015, May 11-13, 2015, Vilnius, Lithuania
Issue:
109
Article no.:
037
Pages:
281-285
No. of pages:
5
Publication type:
Abstract and Fulltext
Published:
2015-05-06
ISBN:
978-91-7519-098-3
Series:
Linköping Electronic Conference Proceedings
ISSN (print):
1650-3686
ISSN (online):
1650-3740
Series:
NEALT Proceedings Series
Publisher:
Linköping University Electronic Press, Linköpings universitet


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In the Latvian language, one word can have tens or even hundreds of surface forms. This is a serious problem for large vocabulary speech recognition. Inclusion of every form in vocabulary will make it intractable, but, on the other hand, even with a vocabulary of 400K, the out-of-vocabulary (OOV) rate will be very high. In this paper, the authors investigate the possibility of using sub-word vocabularies where words are split into frequent and common parts. The results of our experiment show that this allows to significantly reduce the OOV rate.

Proceedings of the 20th Nordic Conference of Computational Linguistics, NODALIDA 2015, May 11-13, 2015, Vilnius, Lithuania

Author:
Askars Salimbajevs, Jevgenijs Strigins
Title:
Using sub-word n-gram models for dealing with OOV in large vocabulary speech recognition for Latvian
References:

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Proceedings of the 20th Nordic Conference of Computational Linguistics, NODALIDA 2015, May 11-13, 2015, Vilnius, Lithuania

Author:
Askars Salimbajevs, Jevgenijs Strigins
Title:
Using sub-word n-gram models for dealing with OOV in large vocabulary speech recognition for Latvian
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