Abstract
The aim of this paper is to demonstrate the complementarity of different scoring methods used in speaker verification. To show that, we implemented two different scoring methods on top of the joint factor analysis model. The results on the telephone part of the NIST’s SRE 2008 core condition show that significant increase in performance can be achieved by fusing likelihood ratio- and support vector machine-based scores.
The presented work has been partly financed by the European Union from the European Social Fund, contract No. 164/2009.
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Vesnicer, B., Gros, J.Ž., Mihelič, F. (2011). Fusion of Discriminative and Generative Scoring Criteria in GMM-Based Speaker Verification. In: Habernal, I., Matoušek, V. (eds) Text, Speech and Dialogue. TSD 2011. Lecture Notes in Computer Science(), vol 6836. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23538-2_18
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DOI: https://doi.org/10.1007/978-3-642-23538-2_18
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