Support vector machine regression for robust speaker verification in mismatching and forensic conditions
Entity
UAM. Departamento de Ingeniería InformáticaPublisher
Springer Berlin HeidelbergDate
2009Citation
10.1007/978-3-642-01793-3_50
Advances in Biometrics: Third International Conference, ICB 2009, Alghero, Italy, June 2-5, 2009. Proceedings. Lecture Notes in Computer Science, Volumen 5558. Springer, 2009. 484-493
ISSN
0302-9743 (print); 1611-3349 (online)ISBN
978-3-642-01792-6 (print); 978-3-642-01793-3 (online)DOI
10.1007/978-3-642-01793-3_50Funded by
This work has been supported by the Spanish Ministry of Education under project TEC2006-13170-C02-01Editor's Version
http://dx.doi.org/10.1007/978-3-642-01793-3_50Subjects
Forensic; GLDS; Robustness; Session variability compensation; Speaker verification; SVM classification; SVM regression; InformáticaNote
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-01793-3_50Proceedings of Third International Conference, ICB 2009, Alghero, Italy
Rights
© Springer-Verlag Berlin Heidelberg 2009Abstract
In this paper we propose the use of Support Vector Machine Regression (SVR) for robust speaker verification in two scenarios: i) strong mismatch in speech conditions and ii) forensic environment. The proposed approach seeks robustness to situations where a proper background database is reduced or not present, a situation typical in forensic cases which has been called database mismatch. For the mismatching condition scenario, we use the NIST SRE 2008 core task as a highly variable environment, but with a mostly representative background set coming from past NIST evaluations. For the forensic scenario, we use the Ahumada III database, a public corpus in Spanish coming from real authored forensic cases collected by Spanish Guardia Civil. We show experiments illustrating the robustness of a SVR scheme using a GLDS kernel under strong session variability, even when no session variability is applied, and especially in the forensic scenario, under database mismatch.
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Google Scholar:Mateos García, Ismael
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Ramos Castro, Daniel
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López Moreno, Ignacio
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González Rodríguez, Joaquín
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