12th Annual Conference of the International Speech Communication Association

Florence, Italy
August 27-31. 2011

Towards Fully Bayesian Speaker Recognition: Integrating Out the Between-Speaker Covariance

Jesús Villalba (1), Niko Brümmer (2)

(1) Universidad de Zaragoza, Spain
(2) Agnitio, South Africa

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.

Full Paper

Bibliographic reference.  Villalba, Jesús / Brümmer, Niko (2011): "Towards fully Bayesian speaker recognition: integrating out the between-speaker covariance", In INTERSPEECH-2011, 505-508.