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Selective Fusion for Speaker Verification in Surveillance

  • Yosef A. Solewicz
  • Moshe Koppel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3495)

Abstract

This paper presents an improved speaker verification technique that is especially appropriate for surveillance scenarios. The main idea is a meta-learning scheme aimed at improving fusion of low- and high-level speech information. While some existing systems fuse several classifier outputs, the proposed method uses a selective fusion scheme that takes into account conveying channel, speaking style and speaker stress as estimated on the test utterance. Moreover, we show that simultaneously employing multi-resolution versions of regular classifiers boosts fusion performance. The proposed selective fusion method aided by multi-resolution classifiers decreases error rate by 30% over ordinary fusion.

Keywords

Support Vector Machine Classifier Speaker Recognition Speaker Verification Universal Background Model Speaker Verification System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Yosef A. Solewicz
    • 1
    • 2
  • Moshe Koppel
    • 1
  1. 1.Dept. of Computer ScienceBar-Ilan UniversityRamat-GanIsrael
  2. 2.Division of Identification and Forensic ScienceIsrael National PoliceJerusalemIsrael

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