Odyssey 2012 - The Speaker and Language Recognition Workshop

June 25-28, 2012

Factor Analysis of Acoustic Features using a Mixture of Probabilistic Principal Component Analyzers for Robust Speaker Verification

Taufiq Hasan, John H. L. Hansen

Center for Robust Speech Systems (CRSS), Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas, Richardson, TX, USA

Robustness due to mismatched train/test conditions is one of the biggest challenges facing speaker recognition today, with transmission channel/handset and additive noise distortion being the most prominent factors. One limitation of the recent speaker recognition systems is that they are based on a latent factor analysis modeling of the GMM mean super-vectors alone. Motivated by the covariance structure of cepstral features, in this study, we develop a factor analysis model in the acoustic feature space instead of the super-vector domain. The proposed technique computes a mixture dependent feature dimensionality reduction transform and is directly applied to the first order Baum-Welch statistics for effective integration with a conventional i-vector-PLDA system. Experimental results on the telephone trials of the NIST SRE 2010 demonstrate the superiority of the proposed scheme.

Full Paper

Bibliographic reference.  Hasan, Taufiq / Hansen, John H. L. (2012): "Factor analysis of acoustic features using a mixture of probabilistic principal component analyzers for robust speaker verification", In Odyssey-2012, 243-247.