Publication Details
Discriminatively Trained i-vector Extractor for Speaker Verification
Burget Lukáš, doc. Ing., Ph.D. (DCGM FIT BUT)
Brümmer Niko (Agnitio)
Plchot Oldřich, Ing., Ph.D. (DCGM FIT BUT)
Matějka Pavel, Ing., Ph.D. (DCGM FIT BUT)
speaker verification, i-vectors, PLDA, discriminative training
We have proposed a technique for discriminative training of the i-vector extractor parameters using cross-entropy as the error function. We have applied the technique both to the original i-vector extractor and to its simplified version. In both cases, the discriminative training was effective, giving higher relative improvement in the simplified case.
We propose a strategy for discriminative training of the ivector extractor in speaker recognition. The original i-vector extractor training was based on the maximum-likelihood generative modeling, where the EM algorithm was used. In our approach, the i-vector extractor parameters are numerically optimized to minimize the discriminative cross-entropy error function. Two versions of the i-vector extraction are studied-the original approach as defined for Joint Factor Analysis, and the simplified version, where orthogonalization of the i-vector extractor matrix is performed.
@INPROCEEDINGS{FITPUB9752,
author = "Ond\v{r}ej Glembek and Luk\'{a}\v{s} Burget and Niko Br{\"{u}}mmer and Old\v{r}ich Plchot and Pavel Mat\v{e}jka",
title = "Discriminatively Trained i-vector Extractor for Speaker Verification",
pages = "137--140",
booktitle = "Proceedings of Interspeech 2011",
journal = "Proceedings of Interspeech - on-line",
volume = 2011,
number = 8,
year = 2011,
location = "Florence, IT",
publisher = "International Speech Communication Association",
ISBN = "978-1-61839-270-1",
ISSN = "1990-9772",
language = "english",
url = "https://www.fit.vut.cz/research/publication/9752"
}