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Speaker verification using speaker- and test-dependent fast score normalization
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DOI：10.1016/j.patrec.2006.06.008.png)
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Abstract
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A novel score normalization scheme for speaker verification is presented. The proposed technique is based on the widely used test-normalization method (Tnorm), which compensates test-dependent variability using a fixed cohort of impostors. The new procedure selects a speaker-dependent subset of impostor models from the fixed cohort using a distance-based criterion. Selection of the sub-cohort is made using a distance measure based on a fast approximation of the Kullback-Leibler (KL) divergence for Gaussian mixture models (GMM). The proposed technique has been called KL-Tnorm, and outperforms Tnorm in computational efficiency. Experimental results using NIST 2005 Speaker Recognition Evaluation protocol also show a stable performance improvement of our method on standard speaker recognition systems. (c) 2006 Elsevier B.V. All rights reserved.
Keywords:
speaker verification
score normalization
Tnorm
Kullback-Leibler divergence
cohort selection
speaker-dependent
test-dependent
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