Discriminatively trained i-vector extractor for speaker verification
Ondřej Glembek, Lukáš Burget, Niko Brümmer, Oldřich Plchot, Pavel Matějka · 2011
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. Index Terms: speaker verification, i-vectors, PLDA, discriminative training