Publication detail
Factorization of Discriminatively Trained i-Vector Extractor for Speaker Recognition
NOVOTNÝ, O. PLCHOT, O. GLEMBEK, O. BURGET, L.
Original Title
Factorization of Discriminatively Trained i-Vector Extractor for Speaker Recognition
Type
conference paper
Language
English
Original Abstract
In this work, we continue in our research on i-vector extractorfor speaker verification (SV) and we optimize its architecturefor fast and effective discriminative training. We were motivatedby computational and memory requirements caused bythe large number of parameters of the original generative ivectormodel. Our aim is to preserve the power of the originalgenerative model, and at the same time focus the model towardsextraction of speaker-related information. We show that it ispossible to represent a standard generative i-vector extractor bya model with significantly less parameters and obtain similarperformance on SV tasks. We can further refine this compactmodel by discriminative training and obtain i-vectors that leadto better performance on various SV benchmarks representingdifferent acoustic domains.
Keywords
SRE
Authors
NOVOTNÝ, O.; PLCHOT, O.; GLEMBEK, O.; BURGET, L.
Released
15. 9. 2019
Publisher
International Speech Communication Association
Location
Graz
ISBN
1990-9772
Periodical
Proceedings of Interspeech
Year of study
2019
Number
9
State
French Republic
Pages from
4330
Pages to
4334
Pages count
5
URL
BibTex
@inproceedings{BUT159998,
author="Ondřej {Novotný} and Oldřich {Plchot} and Ondřej {Glembek} and Lukáš {Burget}",
title="Factorization of Discriminatively Trained i-Vector Extractor for Speaker Recognition",
booktitle="Proceedings of Interspeech",
year="2019",
journal="Proceedings of Interspeech",
volume="2019",
number="9",
pages="4330--4334",
publisher="International Speech Communication Association",
address="Graz",
doi="10.21437/Interspeech.2019-1757",
issn="1990-9772",
url="https://www.isca-speech.org/archive/Interspeech_2019/pdfs/1757.pdf"
}Documents