Publication detail

Neural Network Bottleneck Features for Language Identification

MATĚJKA, P. ZHANG, L. NG, T. MALLIDI, S. GLEMBEK, O. MA, J. ZHANG, B.

Original Title

Neural Network Bottleneck Features for Language Identification

Type

conference paper

Language

English

Original Abstract

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.

Keywords

language identification, noisy speech, robust feature extraction

Authors

MATĚJKA, P.; ZHANG, L.; NG, T.; MALLIDI, S.; GLEMBEK, O.; MA, J.; ZHANG, B.

RIV year

2014

Released

16. 6. 2014

Publisher

International Speech Communication Association

Location

Joensuu

ISBN

2312-2846

Periodical

Proceedings of Odyssey: The Speaker and Language Recognition Workshop Odyssey 2014, Joensuu, Finland

Year of study

2014

Number

6

State

Republic of Finland

Pages from

299

Pages to

304

Pages count

6

URL

http://www.fit.vutbr.cz/research/groups/speech/publi/2014/matejka_odyssey2014_299-304-35.pdf

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"
}