Full-covariance UBM and heavy-tailed PLDA in i-vector speaker verification
Pavel Matějka, Ondřej Glembek, Fabio Castaldo, Md. Jahangir Alam, Oldřich Plchot, Patrick J Kenny, Lukáš Burget, Jaň Černocký · 2011
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.