Support Vector Machine Regression for Robust Speaker Verification in Mismatching and Forensic Conditions

  • Authors:

  • Ismael Mateos-Garcia;Daniel Ramos;Ignacio Lopez-Moreno;Joaquin Gonzalez-Rodriguez

  • Affiliations:

  • ATVS --- Biometric Recognition Group, Escuela Politecnica Superior, Universidad Autonoma de Madrid, Madrid, Spain 28049;ATVS --- Biometric Recognition Group, Escuela Politecnica Superior, Universidad Autonoma de Madrid, Madrid, Spain 28049;ATVS --- Biometric Recognition Group, Escuela Politecnica Superior, Universidad Autonoma de Madrid, Madrid, Spain 28049;ATVS --- Biometric Recognition Group, Escuela Politecnica Superior, Universidad Autonoma de Madrid, Madrid, Spain 28049

  • Venue:
  • ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
  • Year:
  • 2009

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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.