11th Annual Conference of the International Speech Communication Association

Makuhari, Chiba, Japan
September 26-30. 2010

Speaker Recognition Using Supervised Probabilistic Principal Component Analysis

Yun Lei, John H. L. Hansen

University of Texas at Dallas, USA

In this study, a supervised probabilistic principal component analysis (SPPCA) model is proposed in order to integrate the speaker label information into a factor analysis approach using the well-known probabilistic principal component analysis (PPCA) model under a support vector machine (SVM) framework. The latent factor from the proposed model is believed to be more discriminative than one from the PPCA model. The proposed model, combined with different types of intersession compensation techniques in the back-end, is evaluated using the National Institute of Standards and Technology (NIST) Speaker Recognition Evaluation (SRE) 2008 data corpus, along with a comparison to the PPCA model.

Full Paper

Bibliographic reference.  Lei, Yun / Hansen, John H. L. (2010): "Speaker recognition using supervised probabilistic principal component analysis", In INTERSPEECH-2010, 382-385.