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Detail publikačního výsledku
FÉR, R.; MATĚJKA, P.; GRÉZL, F.; PLCHOT, O.; VESELÝ, K.; ČERNOCKÝ, J.
Originální název
Multilingually Trained Bottleneck Features in Spoken Language Recognition
Anglický název
Druh
Článek WoS
Originální abstrakt
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.
Anglický abstrakt
Klíčová slova
Multilingual training, Bottleneck features, Spoken language recognition
Klíčová slova v angličtině
Autoři
Rok RIV
2018
Vydáno
25.07.2017
ISSN
0885-2308
Periodikum
COMPUTER SPEECH AND LANGUAGE
Svazek
2017
Číslo
46
Stát
Spojené království Velké Británie a Severního Irska
Strany od
252
Strany do
267
Strany počet
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" }
Dokumenty
fer_CSL2017