Conference paper

MATĚJKA Pavel, PLCHOT Oldřich, NOVOTNÝ Ondřej, CUMANI Sandro, LOZANO-DIEZ Alicia, SLAVÍČEK Josef, DIEZ Sánchez Mireia, GRÉZL František, GLEMBEK Ondřej, KAMSALI Veera Mounika, SILNOVA Anna, BURGET Lukáš, ONDEL Lucas, KESIRAJU Santosh and ROHDIN Johan A.. BUT- PT System Description for NIST LRE 2017. In: Proceedings of NIST Language Recognition Workshop 2017. Orlando, Florida: National Institute of Standards and Technology, 2017, pp. 1-6.
Publication language:english
Original title:BUT- PT System Description for NIST LRE 2017
Pages:1-6
Proceedings:Proceedings of NIST Language Recognition Workshop 2017
Conference:NIST Language Recognition Workshop 2017
Place:Orlando, Florida, US
Year:2017
Publisher:National Institute of Standards and Technology
URL:http://www.fit.vutbr.cz/research/groups/speech/publi/2017/matejka_lre17-system-description.pdf [PDF]
Keywords
speech recognition, language recognition
Annotation
This article is about the BUT - PT System Description for the NIST LRE 2017 evaluation. We have built over 30 systems for this evaluation with the main focus to build a single best system. We experimented with denoising NN, automatic discovery units, different flavors of phonotactic systems, different backends, different sizes of i-vector systems, different BN features, NN embeddings and frame level language classifiers. The evaluation plan stated "Teams are encouraged to report whether and how having access to the development set helped improve the performance". The development data helped mainly in the final classifier and also helped in the decision process which techniques to use and which to fuse because our test set consisted of this data.
Abstract
Our submission is a collaborative effort of BUT, Politecnico di Torino, Universidad Autonoma de Madrid and Phonexia. The main body of work was conducted during end of September and beginning of October 2017 when the whole team met in Brno and all members were closely working together with common datasets. All of our individual systems rely on the bottleneck features[1, 2] (BNF) as frontends. Most of our systems are still based on i-vectors and subsequent generative classifier. We also complement the classical i-vector based systems with a system based on embeddings obtained from discriminatively trained end-toend LRE system. Finally, the primary submission is a fusion of four systems where we utilize two different BNF extractors, non-linear processing of i-vectors and embeddings obtained from the discriminative system.
BibTeX:
@INPROCEEDINGS{
   author = {Pavel Mat{\v{e}}jka and Old{\v{r}}ich Plchot and
	Ond{\v{r}}ej Novotn{\'{y}} and Sandro Cumani and
	Alicia Lozano-Diez and Josef Slav{\'{i}}{\v{c}}ek
	and Mireia S{\'{a}}nchez Diez and Franti{\v{s}}ek
	Gr{\'{e}}zl and Ond{\v{r}}ej Glembek and Mounika
	Veera Kamsali and Anna Silnova and
	Luk{\'{a}}{\v{s}} Burget and Lucas Ondel and
	Santosh Kesiraju and A. Johan Rohdin},
   title = {BUT- PT System Description for NIST LRE 2017},
   pages = {1--6},
   booktitle = {Proceedings of NIST Language Recognition Workshop 2017},
   year = {2017},
   location = {Orlando, Florida, US},
   publisher = {National Institute of Standards and Technology},
   language = {english},
   url = {http://www.fit.vutbr.cz/research/view_pub.php.en?id=11655}
}

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