Variational bayes logistic regression as regularized fusion for NIST SRE 2010

Ville Hautamäki, Kong Aik Lee, Anthony Larcher, Tomi Kinnunen, Bin Ma, Haizhou Li

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

6 Citations (Scopus)


Fusion of the base classifiers is seen as a way to achieve high performance in state-of-the-art speaker verification systems. Typically, we are looking for base classifiers that would be complementary. We might also be interested in reinforcing good base classifiers by including others that are similar to them. In any case, the final ensemble size is typically small and has to be formed based on some rules of thumb. We are interested to find out a subset of classifiers that has a good generalization performance. We approach the problem from sparse learning point of view. We assume that the true, but unknown, fusion weights are sparse. As a practical solution, we regularize weighted logistic regression loss function by elastic-net and LASSO constraints. However, all regularization methods have an additional parameter that controls the amount of regularization employed. This needs to be separately tuned. In this work, we use variational Bayes approach to automatically obtain sparse solutions without additional cross-validation. Variational Bayes method improves the baseline method in 3 out of 4 sub-conditions.

Original languageEnglish
Title of host publicationOdyssey 2012 - Speaker and Language Recognition Workshop
EditorsHaizhou Li, Bin Ma, Kong Aik Lee
PublisherChinese and Oriental Languages Information Processing Society (COLIPS), Speaker and Language Characterization SIG
Number of pages7
ISBN (Electronic)9789810730932
Publication statusPublished - Jun 2012
Externally publishedYes
EventSpeaker and Language Recognition Workshop, Odyssey 2012 - Singapore, Singapore
Duration: 25 Jun 201228 Jun 2012

Publication series

NameOdyssey 2012 - Speaker and Language Recognition Workshop


ConferenceSpeaker and Language Recognition Workshop, Odyssey 2012


  • Compressed sensing
  • Linear fusion
  • Logistic regression
  • Regularization
  • Speaker verification

ASJC Scopus subject areas

  • Signal Processing
  • Software
  • Human-Computer Interaction


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