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Detail publikačního výsledku
KARAFIÁT, M.; BURGET, L.; MATĚJKA, P.; GLEMBEK, O.; ČERNOCKÝ, J.
Originální název
iVector-Based Discriminative Adaptation for Automatic Speech Recognition
Anglický název
Druh
Stať ve sborníku mimo WoS a Scopus
Originální abstrakt
The iVector is alow-dimensional fixed-length representation of information about speaker and acoustic environment. Toutilize iVectors for adaptation, region dependent linear transforms(RDLT) are discriminatively trained using the MPE criterion on largeamounts of annotated data to extract the relevant information fromiVectors and to compensate speech features. The approach was tested onstandard CTS data. We found it to be complementary to common adaptationtechniques. On a well-tuned RDLT system with standard CMLLR adaptationwe reached an 0.8% additive absolute WER improvement.
Anglický abstrakt
Klíčová slova
Automatic speech recognition, I-vector, Discriminative adaptation
Klíčová slova v angličtině
Autoři
Rok RIV
2012
Vydáno
11.12.2011
Nakladatel
IEEE Signal Processing Society
Místo
Hilton Waikoloa Village, Big Island, Hawaii
ISBN
978-1-4673-0366-8
Kniha
Proceedings of ASRU 2011
Strany od
152
Strany do
157
Strany počet
6
URL
http://www.fit.vutbr.cz/research/groups/speech/publi/2011/karafiat_asru2011_00152.pdf
BibTex
@inproceedings{BUT76442, author="Martin {Karafiát} and Lukáš {Burget} and Pavel {Matějka} and Ondřej {Glembek} and Jan {Černocký}", title="iVector-Based Discriminative Adaptation for Automatic Speech Recognition", booktitle="Proceedings of ASRU 2011", year="2011", pages="152--157", publisher="IEEE Signal Processing Society", address="Hilton Waikoloa Village, Big Island, Hawaii", isbn="978-1-4673-0366-8", url="http://www.fit.vutbr.cz/research/groups/speech/publi/2011/karafiat_asru2011_00152.pdf" }