11th Annual Conference of the International Speech Communication Association

Makuhari, Chiba, Japan
September 26-30. 2010

Semi-Supervised Training of Gaussian Mixture Models by Conditional Entropy Minimization

Jui-Ting Huang, Mark Hasegawa-Johnson

University of Illinois at Urbana-Champaign, USA

In this paper, we propose a new semi-supervised training method for Gaussian Mixture Models. We add a conditional entropy minimizer to the maximum mutual information criteria, which enables to incorporate unlabeled data in a discriminative training fashion. The training method is simple but surprisingly effective. The preconditioned conjugate gradient method provides a reasonable convergence rate for parameter update. The phonetic classification experiments on the TIMIT corpus demonstrate significant improvements due to unlabeled data via our training criteria.

Full Paper

Bibliographic reference.  Huang, Jui-Ting / Hasegawa-Johnson, Mark (2010): "Semi-supervised training of Gaussian mixture models by conditional entropy minimization", In INTERSPEECH-2010, 1353-1356.