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ROHDIN, J. SILNOVA, A. DIEZ SÁNCHEZ, M. PLCHOT, O. MATĚJKA, P. BURGET, L. GLEMBEK, O.
Originální název
End-to-end DNN based text-independent speaker recognition for long and short utterances
Typ
článek v časopise ve Web of Science, Jimp
Jazyk
angličtina
Originální abstrakt
Recently several end-to-end speaker verification systems based on deep neural networks (DNNs) have been proposed. These systems have been proven to be competitive for text-dependent tasks as well as for text-independent tasks with short utterances. However, for text-independent tasks with longer utterances, end-to-end systems are still outperformed by standard i-vector + PLDA systems. In this work, we present an end-to-end speaker verification system that is initialized to mimic an i-vector + PLDA baseline. The system is then further trained in an end-to-end manner but regularized so that it does not deviate too far from the initial system. In this way we mitigate overfitting which normally limits the performance of end-to-end systems. The proposed system outperforms the i-vector + PLDA baseline on both long and short duration utterances.
Klíčová slova
Speaker verification, DNN, End-to-end, Text-independent, i-vector, PLDA
Autoři
ROHDIN, J.; SILNOVA, A.; DIEZ SÁNCHEZ, M.; PLCHOT, O.; MATĚJKA, P.; BURGET, L.; GLEMBEK, O.
Vydáno
1. 1. 2020
ISSN
0885-2308
Periodikum
COMPUTER SPEECH AND LANGUAGE
Ročník
2020
Číslo
59
Stát
Spojené království Velké Británie a Severního Irska
Strany od
22
Strany do
35
Strany počet
14
URL
https://www.sciencedirect.com/science/article/pii/S0885230818303632
BibTex
@article{BUT158088, author="Johan Andréas {Rohdin} and Anna {Silnova} and Mireia {Diez Sánchez} and Oldřich {Plchot} and Pavel {Matějka} and Lukáš {Burget} and Ondřej {Glembek}", title="End-to-end DNN based text-independent speaker recognition for long and short utterances", journal="COMPUTER SPEECH AND LANGUAGE", year="2020", volume="2020", number="59", pages="22--35", doi="10.1016/j.csl.2019.06.002", issn="0885-2308", url="https://www.sciencedirect.com/science/article/pii/S0885230818303632" }
Dokumenty
rohdin_elsevier_Journal_Paper_2020_18303632.pdf