Data-driven impostor selection for T-norm score normalisation and the background dataset in SVM-based speaker verification


Autoria(s): McLaren, Mitchell L.; Vogt, Robert J.; Baker, Brendan J.; Sridharan, Sridha
Contribuinte(s)

Massimo, Tistarelli

Nixon, Mark S.

Data(s)

2009

Resumo

A data-driven background dataset refinement technique was recently proposed for SVM based speaker verification. This method selects a refined SVM background dataset from a set of candidate impostor examples after individually ranking examples by their relevance. This paper extends this technique to the refinement of the T-norm dataset for SVM-based speaker verification. The independent refinement of the background and T-norm datasets provides a means of investigating the sensitivity of SVM-based speaker verification performance to the selection of each of these datasets. Using refined datasets provided improvements of 13% in min. DCF and 9% in EER over the full set of impostor examples on the 2006 SRE corpus with the majority of these gains due to refinement of the T-norm dataset. Similar trends were observed for the unseen data of the NIST 2008 SRE.

Formato

application/pdf

Identificador

http://eprints.qut.edu.au/29485/

Publicador

Springer

Relação

http://eprints.qut.edu.au/29485/1/c29485.pdf

DOI:10.1007/978-3-642-01793-3_49

McLaren, Mitchell L., Vogt, Robert J., Baker, Brendan J., & Sridharan, Sridha (2009) Data-driven impostor selection for T-norm score normalisation and the background dataset in SVM-based speaker verification. In Massimo, Tistarelli & Nixon, Mark S. (Eds.) Advances in Biometrics : Third International Conferences, ICB 2009, Alghero, Italy, June 2-5, 2009, Proceedings. Springer , Berlin Heidelberg, pp. 474-483.

Direitos

Copyright 2009 Springer

The original publication is available at SpringerLink http://www.springerlink.com

Fonte

Faculty of Built Environment and Engineering; Information Security Institute; School of Engineering Systems

Palavras-Chave #Speaker recognition #Data selection #Support vector machines #Score Normalisation
Tipo

Book Chapter