On cohort selection for speaker verification

Yaniv Zigel, Arnon Cohen

Research output: Contribution to conferencePaperpeer-review

14 Scopus citations


Speaker verification systems require some kind of background model to reliably perform the verification task. Several algorithms have been proposed for the selection of cohort models to form a background model. This paper proposes a new cohort selection method called the Close Impostor Clustering (CIC). The new method is shown to outperform several other methods in a text-dependent verification task. Several normalization methods are also compared. With three cohort models and the best scorenormalization method, the CIC yielded an average Equal Error Rate (EER) of 0.8%, while the second best method (Maximally-Spread Close, MSC) yielded average EER of 1.1%.

Original languageEnglish
Number of pages4
StatePublished - 1 Jan 2003
Event8th European Conference on Speech Communication and Technology, EUROSPEECH 2003 - Geneva, Switzerland
Duration: 1 Sep 20034 Sep 2003


Conference8th European Conference on Speech Communication and Technology, EUROSPEECH 2003

ASJC Scopus subject areas

  • Computer Science Applications
  • Software
  • Linguistics and Language
  • Communication


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