Conference paper

PLCHOT Oldřich, MATĚJKA Pavel, FÉR Radek, GLEMBEK Ondřej, NOVOTNÝ Ondřej, PEŠÁN Jan, VESELÝ Karel, ONDEL Lucas, KARAFIÁT Martin, GRÉZL František, KESIRAJU Santosh, BURGET Lukáš, BRUMMER Niko, SWART Albert du Preez, CUMANI Sandro, MALLIDI Sri Harish and LI Ruizhi. BAT System Description for NIST LRE 2015. In: Proceedings of Odyssey 2016, The Speaker and Language Recognition Workshop. Bilbao: International Speech Communication Association, 2016, pp. 166-173. ISSN 2312-2846. Available from:
Publication language:english
Original title:BAT System Description for NIST LRE 2015
Title (cs):Popis BAT systému pro NIST LRE 2015 evaluace
Proceedings:Proceedings of Odyssey 2016, The Speaker and Language Recognition Workshop
Conference:Odyssey 2016
Place:Bilbao, ES
Journal:Proceedings of Odyssey: The Speaker and Language Recognition Workshop, Vol. 2016, No. 06, 4 Rue des Fauvettes - Lous Tourils, F-66390 BAIXAS, FR
Publisher:International Speech Communication Association
BAT System Description,  NIST LRE
In this work, we have described our efforts in the NIST LRE 2015. The most difficult part of this evaluation was to deal with limited amount of data and the results show that the proper analysis in this direction is necessary. We have built over 20 systems for this evaluation. We have experimented with de-noising NN, automatic unit discovery, different flavors of phonotactic systems, backends, sizes of ivector systems, feature sets, BN features or frame level language classifiers. We used up to 6 systems in the fusion. The performance of our best system reached Cavg of 16.9% on the fixed training data condition and 13.9% (11.9% after post-evaluation analysis) on the open training data condition.
In this paper, we summarize our efforts in the NIST Language
Recognition (LRE) 2015 Evaluations which resulted in
systems providing very competitive performance. We provide
both the descriptions and the analysis of the systems that we
included in our submission. We start by detailed description of
the datasets that we used for training and development, and we
follow by describing the models and methods that were used to
produce the final scores. These include the front-end (i.e., the
voice activity detection and feature extraction), the back-end
(i.e., the final classifier), and the calibration and fusion stages.
Apart from the techniques commonly used in the field (such as
i-vectors, DNN Bottle-Neck features, NN classifiers, Gaussian
Back-ends, etc.), we present less-common methods, such as Sequence
Summarizing Neural Networks (SSNN), and Automatic
Unit Discovery. We present the performance of the systems
both on the Fixed condition (where participants are required to
use predefined data sets only), and the Open condition (where
participants are allowed to use any publicly available resource)
of the NIST LRE 2015.
   author = {Old{\v{r}}ich Plchot and Pavel Mat{\v{e}}jka and
	Radek F{\'{e}}r and Ond{\v{r}}ej Glembek and
	Ond{\v{r}}ej Novotn{\'{y}} and Jan
	Pe{\v{s}}{\'{a}}n and Karel Vesel{\'{y}} and Lucas
	Ondel and Martin Karafi{\'{a}}t and
	Franti{\v{s}}ek Gr{\'{e}}zl and Santosh Kesiraju
	and Luk{\'{a}}{\v{s}} Burget and Niko Brummer and
	Preez du Albert Swart and Sandro Cumani and Harish
	Sri Mallidi and Ruizhi Li},
   title = {BAT System Description for NIST LRE 2015},
   pages = {166--173},
   booktitle = {Proceedings of Odyssey 2016, The Speaker and Language
	Recognition Workshop},
   journal = {Proceedings of Odyssey: The Speaker and Language Recognition
   volume = 2016,
 number = 06,
   year = 2016,
   location = {Bilbao, ES},
   publisher = {International Speech Communication Association},
   ISSN = {2312-2846},
   doi = {10.21437/Odyssey.2016-24},
   language = {english},
   url = {}

Your IPv4 address:
Switch to https