Result Details
Neural network based language models for highly inflective languages
Kopecký Jiří, Ing.
Burget Lukáš, doc. Ing., Ph.D., DCGM (FIT)
Glembek Ondřej, Ing., Ph.D., DCGM (FIT)
Černocký Jan, prof. Dr. Ing., DCGM (FIT)
The paper is on neural network based language models for highly inflective languages
language modeling, neural networks, inflectivelanguages
Speech recognition of inflectional and morphologically rich languages like Czech is currently quite a challenging task, because simple n-gram techniques are unable to capture important regularities in the data. Several possible solutions were proposed, namely class based models, factored models, decision trees and neural networks. This paper describes improvements obtained in recognition of spoken Czech lectures using languagemodels based on neural networks. Relative reductions in word error rate are more than 15% over baseline obtained with adapted 4-gram backoff language model using modified Kneser-Ney smoothing.
@inproceedings{BUT33797,
author="Tomáš {Mikolov} and Jiří {Kopecký} and Lukáš {Burget} and Ondřej {Glembek} and Jan {Černocký}",
title="Neural network based language models for highly inflective languages",
booktitle="Proc. ICASSP 2009",
year="2009",
pages="1--4",
publisher="IEEE Signal Processing Society",
address="Taipei",
isbn="978-1-4244-2354-5",
url="http://www.fit.vutbr.cz/research/groups/speech/publi/2009/mikolov_ic2009_nnlm_4.pdf"
}
Research and development of corpus and speech technologies in new generation of electronic dictionaries, MPO, TANDEM, FT-TA3/006, start: 2006-06-01, end: 2009-12-31, completed
Security-Oriented Research in Information Technology, MŠMT, Institucionální prostředky SR ČR (např. VZ, VC), MSM0021630528, start: 2007-01-01, end: 2013-12-31, running