Towards fully Bayesian speaker recognition: integrating out the between-speaker covariance
Jesús Villalba, Niko Brümmer · 2011
We propose a variational Bayes solution to integrate out the model parameters in a generative i-vector speaker recognizer. The existing state-of-the-art in generative i-vector modelling plugs in fixed maximum-likelihood point-estimates of model parameters. This recipe may suffer from over-fitting of especially the between-speaker covariance. We show how to integrate out the between-speaker covariance and demonstrate dramatic improvements on NIST SRE 2010.