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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 extractor for speaker verification (SV) and we optimize its architecture for fast and effective discriminative training. We were motivated by computational and memory requirements caused by the large number of parameters of the original generative ivector model. Our aim is to preserve the power of the original generative model, and at the same time focus the model towards extraction of speaker-related information. We show that it is possible to represent a standard generative i-vector extractor by a model with significantly less parameters and obtain similar performance on SV tasks. We can further refine this compact model by discriminative training and obtain i-vectors that lead to better performance on various SV benchmarks representing different 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
https://www.isca-speech.org/archive/Interspeech_2019/pdfs/1757.pdf
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" }