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MAP estimation of subspace transform for speaker recognition

We propose using the maximum- a-posteriori (MAP) estimation of subspace transform for speaker recognition. The linear transform is deﬁned on the mean vectors of the Gaussian mixture model (GMM), where transform matrices and bias vectors are associated with separate regression classes so that both can be estimated with sufﬁcient statistics given limited training data. The transform matrices are further deﬁned as a linear combination of a set of basis transforms so that the weights are parameters to be estimated. We characterize the speakers with the transform parameters and model them using support vector machine (SVM). Experiments on the 2008 NIST SRE task illustrate the effectiveness of the method.