Detail publikace
Neural Network Bottleneck Features for Language Identification
MATĚJKA, P. ZHANG, L. NG, T. MALLIDI, S. GLEMBEK, O. MA, J. ZHANG, B.
Originální název
Neural Network Bottleneck Features for Language Identification
Typ
článek ve sborníku ve WoS nebo Scopus
Jazyk
angličtina
Originální abstrakt
We have presented the bottleneck features in the context of Languageidentification. It combines benefits of both phonotacticand acoustic system. Usually, the phonotactic system is favorablefor the long duration files, while acoustic for the short ones.This approach takes the advantage of both. In addition, we canalso use modeling of context dependent phonemes in bottleneckfeatures. This brings very nice improvement over the contextindependent phonemes.
Klíčová slova
language identification, noisy speech, robustfeature extraction
Autoři
MATĚJKA, P.; ZHANG, L.; NG, T.; MALLIDI, S.; GLEMBEK, O.; MA, J.; ZHANG, B.
Rok RIV
2014
Vydáno
16. 6. 2014
Nakladatel
International Speech Communication Association
Místo
Joensuu
ISSN
2312-2846
Periodikum
Proceedings of Odyssey: The Speaker and Language Recognition Workshop Odyssey 2014, Joensuu, Finland
Ročník
2014
Číslo
6
Stát
Finská republika
Strany od
299
Strany do
304
Strany počet
6
URL
BibTex
@inproceedings{BUT111630,
author="Pavel {Matějka} and Le {Zhang} and Tim {Ng} and Sri Harish {Mallidi} and Ondřej {Glembek} and Jeff {Ma} and Bing {Zhang}",
title="Neural Network Bottleneck Features for Language Identification",
booktitle="Proceedings of Odyssey 2014",
year="2014",
journal="Proceedings of Odyssey: The Speaker and Language Recognition Workshop Odyssey 2014, Joensuu, Finland",
volume="2014",
number="6",
pages="299--304",
publisher="International Speech Communication Association",
address="Joensuu",
issn="2312-2846",
url="http://www.fit.vutbr.cz/research/groups/speech/publi/2014/matejka_odyssey2014_299-304-35.pdf"
}