Publication Details
Full-covariance UBM and Heavy-tailed PLDA in I-Vector Speaker Verification
Glembek Ondřej, Ing., Ph.D. (DCGM FIT BUT)
Castaldo Fabio (POLITO)
Alam Jahangir (CRIM)
Plchot Oldřich, Ing., Ph.D. (DCGM FIT BUT)
Kenny Patrick (CRIM)
Burget Lukáš, doc. Ing., Ph.D. (DCGM FIT BUT)
Černocký Jan, prof. Dr. Ing. (DCGM FIT BUT)
GMM, speaker recognition, PLDA, heavytailed PLDA, full-covariance UBM, i-vectors
The work we presented aims at the best performance of the single stand alone system. We have presented full-covariance UBM and i-vector extraction with different kind of modeling. Our analysis shows that for the best performance it is necessary to have fullcovariance i-vector without any approximation.
In this paper, we describe recent progress in i-vector based speaker verification. The use of universal background models (UBM) with full-covariance matrices is suggested and thoroughly experimentally tested. The i-vectors are scored using a simple cosine distance and advanced techniques such as Probabilistic Linear Discriminant Analysis (PLDA) and heavy-tailed variant of PLDA (PLDA-HT). Finally, we investigate into dimensionality reduction of i-vectors before entering the PLDA-HT modeling. The results are very competitive: on NIST 2010 SRE task, the results of a single full-covariance LDA-PLDA-HT system approach those of complex fused system.
@INPROCEEDINGS{FITPUB9657, author = "Pavel Mat\v{e}jka and Ond\v{r}ej Glembek and Fabio Castaldo and Jahangir Alam and Old\v{r}ich Plchot and Patrick Kenny and Luk\'{a}\v{s} Burget and Jan \v{C}ernock\'{y}", title = "Full-covariance UBM and Heavy-tailed PLDA in I-Vector Speaker Verification", pages = "4828--4831", booktitle = "Proceedings of the 2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011", year = 2011, location = "Praha, CZ", publisher = "IEEE Signal Processing Society", ISBN = "978-1-4577-0537-3", doi = "10.1109/ICASSP.2011.5947436", language = "english", url = "https://www.fit.vut.cz/research/publication/9657" }