Publication Details
End-to-end DNN based text-independent speaker recognition for long and short utterances
Silnova Anna, MSc., Ph.D. (DCGM FIT BUT)
Diez Sánchez Mireia, M.Sc., Ph.D. (DCGM FIT BUT)
Plchot Oldřich, Ing., Ph.D. (DCGM FIT BUT)
Matějka Pavel, Ing., Ph.D. (DCGM FIT BUT)
Burget Lukáš, doc. Ing., Ph.D. (DCGM FIT BUT)
Glembek Ondřej, Ing., Ph.D. (DCGM FIT BUT)
Speaker verification, DNN, End-to-end, Text-independent, i-vector, PLDA
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.
@ARTICLE{FITPUB12038,
author = "A. Johan Rohdin and Anna Silnova and Mireia S\'{a}nchez Diez and Old\v{r}ich Plchot and Pavel Mat\v{e}jka and Luk\'{a}\v{s} Burget and Ond\v{r}ej Glembek",
title = "End-to-end DNN based text-independent speaker recognition for long and short utterances",
pages = "22--35",
journal = "Computer Speech and Language",
volume = 2020,
number = 59,
year = 2020,
ISSN = "0885-2308",
doi = "10.1016/j.csl.2019.06.002",
language = "english",
url = "https://www.fit.vut.cz/research/publication/12038"
}