Conference paper

FÉR Radek, MATĚJKA Pavel, GRÉZL František, PLCHOT Oldřich and ČERNOCKÝ Jan. Multilingual Bottleneck Features for Language Recognition. In: Proceedings of Interspeech 2015. Dresden: International Speech Communication Association, 2015, pp. 389-393. ISBN 978-1-5108-1790-6. ISSN 1990-9772.
Publication language:english
Original title:Multilingual Bottleneck Features for Language Recognition
Title (cs):Multilingvální příznaky z neuronové sítě s úzkým hrdlem pro rozpoznávání jazyka
Proceedings:Proceedings of Interspeech 2015
Conference:INTERSPEECH 2015
Place:Dresden, DE
Journal:Proceedings of Interspeech, Vol. 2015, No. 09, FR
Publisher:International Speech Communication Association
multilingual training, stacked bottleneck features, language identification
In this work, we applied multilingual training paradigm of SBN neural networks to extract linguistically rich features.
In this paper, we investigate Multilingual Stacked Bottleneck Features (SBN) in language recognition domain. These features are extracted using bottleneck neural networks trained on data from multiple languages. Previous results have shown benefits of multilingual training of SBN feature extractor for speech recognition. Here we focus on its impact on language recognition. We present results obtained with monolingual and multilingual networks, and their fusions. Using multilingual features, we obtain 16% relative improvement on 3 s condition of NIST LRE09 dataset with respect to features trained on a single language
   author = {Radek F{\'{e}}r and Pavel Mat{\v{e}}jka and
	Franti{\v{s}}ek Gr{\'{e}}zl and Old{\v{r}}ich
	Plchot and Jan {\v{C}}ernock{\'{y}}},
   title = {Multilingual Bottleneck Features for Language
   pages = {389--393},
   booktitle = {Proceedings of Interspeech 2015},
   journal = {Proceedings of Interspeech},
   volume = 2015,
 number = 09,
   year = 2015,
   location = {Dresden, DE},
   publisher = {International Speech Communication Association},
   ISBN = {978-1-5108-1790-6},
   ISSN = {1990-9772},
   language = {english},
   url = {}

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