Publication Details

Neural Network Bottleneck Features for Language Identification

MATĚJKA, P.; ZHANG, L.; NG, T.; MALLIDI, S.; GLEMBEK, O.; MA, J.; ZHANG, B. Neural Network Bottleneck Features for Language Identification. In Proceedings of Odyssey 2014. Proceedings of Odyssey: The Speaker and Language Recognition Workshop Odyssey 2014, Joensuu, Finland. Joensuu: International Speech Communication Association, 2014. p. 299-304. ISSN: 2312-2846.
Czech title
Příznaky z neuronové sítě s úzkým hrdlem pro identifikaci jazyka
Type
conference paper
Language
English
Authors
Matějka Pavel, Ing., Ph.D. (DCGM)
Zhang Le (FIT)
Ng Tim
Mallidi Sri Harish (FIT)
Glembek Ondřej, Ing., Ph.D.
Ma Jeff
Zhang Bing
URL
Keywords

language identification, noisy speech, robust feature extraction

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.

Annotation

This paper presents the application of Neural Network Bottleneck (BN) features in Language Identification (LID). BN features are generally used for Large Vocabulary Speech Recognition in conjunction with conventional acoustic features, such as MFCC or PLP.We compare the BN features to several common types of acoustic features used in the state-of-the-art LID systems. The test set is from DARPA RATS (Robust Automatic Transcription of Speech) program, which seeks to advance state-of-the-art detection capabilities on audio from highly degraded radio communication channels. On this type of noisy data, we show that in average, the BN features provide a 45% relative improvement in the Cavgor Equal Error Rate (EER) metrics across several test duration conditions, with respect to our single best acoustic features.

Published
2014
Pages
299–304
Journal
Proceedings of Odyssey: The Speaker and Language Recognition Workshop Odyssey 2014, Joensuu, Finland, vol. 2014, no. 6, ISSN 2312-2846
Proceedings
Proceedings of Odyssey 2014
Publisher
International Speech Communication Association
Place
Joensuu
EID Scopus
2-s2.0-85073163761
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"
}
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