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.

• Type: Journal
• Year: 2011
• Where: ICASSP
• Authors: Lukas Burget, Oldrich Plchot, Sandro Cumani, Ondrej Glembek, Pavel Matejka, Niko Brümmer
• Keywords: Discriminative Training, Discriminative Training Criterion, ICASSP 2011, Signal Processing, Speaker Verification