Sciweavers

Share
ICASSP
2011
IEEE

Discriminatively trained Probabilistic Linear Discriminant Analysis for speaker verification

11 years 5 months ago
Discriminatively trained Probabilistic Linear Discriminant Analysis for speaker verification
Recently, i-vector extraction and Probabilistic Linear Discriminant Analysis (PLDA) have proven to provide state-of-the-art speaker verification performance. In this paper, the speaker verification score for a pair of i-vectors representing a trial is computed with a functional form derived from the successful PLDA generative model. In our case, however, parameters of this function are estimated based on a discriminative training criterion. We propose to use the objective function to directly address the task in speaker verification: discrimination between same-speaker and different-speaker trials. Compared with a baseline which uses a generatively trained PLDA model, discriminative training provides up to 40% relative improvement on the NIST SRE 2010 evaluation task.
Lukas Burget, Oldrich Plchot, Sandro Cumani, Ondre
Added 20 Aug 2011
Updated 20 Aug 2011
Type Journal
Year 2011
Where ICASSP
Authors Lukas Burget, Oldrich Plchot, Sandro Cumani, Ondrej Glembek, Pavel Matejka, Niko Brümmer
Comments (0)
books