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Support vector machine regression for robust speaker verification in mismatching and forensic conditions

Author
Mateos García, Ismael; Ramos Castro, Danieluntranslated; López Moreno, Ignacio; González Rodríguez, Joaquínuntranslated
Entity
UAM. Departamento de Ingeniería Informática
Publisher
Springer Berlin Heidelberg
Date
2009
Citation
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_50
Funded by
This work has been supported by the Spanish Ministry of Education under project TEC2006-13170-C02-01
Editor's Version
http://dx.doi.org/10.1007/978-3-642-01793-3_50
Subjects
Forensic; GLDS; Robustness; Session variability compensation; Speaker verification; SVM classification; SVM regression; Informática
URI
http://hdl.handle.net/10486/664235
Note
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-01793-3_50
Proceedings of Third International Conference, ICB 2009, Alghero, Italy
Rights
© Springer-Verlag Berlin Heidelberg 2009

Abstract

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 - Ramos Castro, Daniel - López Moreno, Ignacio - González Rodríguez, Joaquín

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  • Producción científica en acceso abierto de la UAM [15083]

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