Publication detail

Multilingually Trained Bottleneck Features in Spoken Language Recognition

FÉR, R. MATĚJKA, P. GRÉZL, F. PLCHOT, O. VESELÝ, K. ČERNOCKÝ, J.

Original Title

Multilingually Trained Bottleneck Features in Spoken Language Recognition

Type

journal article in Web of Science

Language

English

Original Abstract

Multilingual training of neural networks has proven to be simple yet effective way to deal with multilingual training corpora. It allows to use several resources to jointly train a language independent representation of features, which can be encoded into low-dimensional feature set by embedding narrow bottleneck layer to the network. In this paper, we analyze such features on the task of spoken language recognition (SLR), focusing on practical aspects of training bottleneck networks and analyzing their integration in SLR. By comparing properties of mono and multilingual features we show the suitability of multilingual training for SLR. The state-of-the-art performance of these features is demonstrated on the NIST LRE09 database.

Keywords

Multilingual training, Bottleneck features, Spoken language recognition

Authors

FÉR, R.; MATĚJKA, P.; GRÉZL, F.; PLCHOT, O.; VESELÝ, K.; ČERNOCKÝ, J.

Released

25. 7. 2017

ISBN

0885-2308

Periodical

COMPUTER SPEECH AND LANGUAGE

Year of study

2017

Number

46

State

United Kingdom of Great Britain and Northern Ireland

Pages from

252

Pages to

267

Pages count

16

URL

http://www.sciencedirect.com/science/article/pii/S0885230816302947

BibTex

@article{BUT144471,
  author="Radek {Fér} and Pavel {Matějka} and František {Grézl} and Oldřich {Plchot} and Karel {Veselý} and Jan {Černocký}",
  title="Multilingually Trained Bottleneck Features in Spoken Language Recognition",
  journal="COMPUTER SPEECH AND LANGUAGE",
  year="2017",
  volume="2017",
  number="46",
  pages="252--267",
  doi="10.1016/j.csl.2017.06.008",
  issn="0885-2308",
  url="http://www.sciencedirect.com/science/article/pii/S0885230816302947"
}

Documents

fer_CSL2017.pdf