Maximum a Posteriori Adaptation of the Centroid Model for Speaker Verification

Ville Hautamäki, Tomi Kinnunen, Ismo Kärkkäinen, Juhani Saastamoinen, Marko Tuononen, Pasi Fränti

Abstract: Maximum a posteriori adapted Gaussian mixture model (GMM-MAP) is widely used in speaker verification. GMMs have three sets of parameters to be adapted: means, covariances, and weights. However, practice has shown that it is sufficient to adapt the means only. Motivated by this, we formulate maximum a posteriori vector quantization (VQ-MAP) procedure which stores and adapts the mean vectors (centroids) only. Experiments on the NIST 2001 and NIST 2006 corpora indicate that VQ-MAP gives comparable accuracy with GMM-MAP with simpler implementation and faster adaptation.