Speaker identification and verification using Gaussian mixture speaker models
Speech Communication
Speaker verification using speaker- and test-dependent fast score normalization
Pattern Recognition Letters
A tutorial on text-independent speaker verification
EURASIP Journal on Applied Signal Processing
Combining Derivative and Parametric Kernels for Speaker Verification
IEEE Transactions on Audio, Speech, and Language Processing
Speaker Model Clustering for Efficient Speaker Identification in Large Population Applications
IEEE Transactions on Audio, Speech, and Language Processing
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Among the various proposed score normalizations, T- and Z-norm are most widely used in speaker verification systems. The main idea in these normalizations is to reduce the variations in impostor scores in order to improve accuracy. These normalizations require selection of a set of cohort models or utterances in order to estimate the impostor score distribution. In this paper we investigate basing this selection on recently-proposed speaker model clusters (SMCs). We evaluate this approach using the NTIMIT and NIST-2002 corpora and compare against T- and Z-norm which use other cohort selection methods. We also propose three new normalization techniques, @D-, @DT- and TC-norm, which also use SMCs to estimate the normalization parameters. Our results show that we can lower the equal error rate and minimum decision cost function with fewer cohort models using SMC-based score normalization approaches.