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 Language identification. It combines benefits of both phonotactic and acoustic system. Usually, the phonotactic system is favorable for the long duration files, while acoustic for the short ones. This approach takes the advantage of both. In addition, we can also use modeling of context dependent phonemes in bottleneck features. This brings very nice improvement over the context independent phonemes.

Klíčová slova

language identification, noisy speech, robust feature 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"
}