Publication detail
End-to-End DNN Based Speaker Recognition Inspired by i-Vector and PLDA
ROHDIN, J. SILNOVA, A. DIEZ SÁNCHEZ, M. PLCHOT, O. MATĚJKA, P. BURGET, L.
Original Title
End-to-End DNN Based Speaker Recognition Inspired by i-Vector and PLDA
Type
conference paper
Language
English
Original Abstract
Recently, several end-to-end speaker verification systems based ondeep neural networks (DNNs) have been proposed. These systemshave been proven to be competitive for text-dependent tasks as wellas for text-independent tasks with short utterances. However, fortext-independent tasks with longer utterances, end-to-end systemsare still outperformed by standard i-vector + PLDA systems. In thiswork, we develop an end-to-end speaker verification system that isinitialized to mimic an i-vector + PLDA baseline. The system isthen further trained in an end-to-end manner but regularized so thatit does not deviate too far from the initial system. In this way wemitigate overfitting which normally limits the performance of endto-end systems. The proposed system outperforms the i-vector +PLDA baseline on both long and short duration utterances.
Keywords
Speaker verification, DNN, end-to-end
Authors
ROHDIN, J.; SILNOVA, A.; DIEZ SÁNCHEZ, M.; PLCHOT, O.; MATĚJKA, P.; BURGET, L.
Released
15. 4. 2018
Publisher
IEEE Signal Processing Society
Location
Calgary
ISBN
978-1-5386-4658-8
Book
Proceedings of ICASSP
Pages from
4874
Pages to
4878
Pages count
5
URL
BibTex
@inproceedings{BUT155046,
author="Johan Andréas {Rohdin} and Anna {Silnova} and Mireia {Diez Sánchez} and Oldřich {Plchot} and Pavel {Matějka} and Lukáš {Burget}",
title="End-to-End DNN Based Speaker Recognition Inspired by i-Vector and PLDA",
booktitle="Proceedings of ICASSP",
year="2018",
pages="4874--4878",
publisher="IEEE Signal Processing Society",
address="Calgary",
doi="10.1109/ICASSP.2018.8461958",
isbn="978-1-5386-4658-8",
url="https://www.fit.vut.cz/research/publication/11724/"
}Documents