Multi-session PLDA scoring of I-vector for partially open-set speaker detection

Kong Aik Lee, Anthony Larcher, Chang Huai You, Bin Ma, Haizhou Li

Research output: Journal article publicationConference articleAcademic researchpeer-review

25 Citations (Scopus)

Abstract

This paper advocates the use of probabilistic linear discriminant analysis (PLDA) for partially open-set detection task with multiple i-vectors enrollment condition. Also referred to as speaker verification, the speaker detection task has always been considered under an open-set scenario. In this paper, a more general partially open-set speaker detection problem in considered, where the imposters might be one of the known speakers previously enrolled to the system. We show how this could be coped with by modifying the definition of the alternative hypothesis in the PLDA scoring function. We also look into the impact of the conditionalindependent assumption as it was used to derive the PLDA scoring function with multiple training i-vectors. Experiments were conducted using the NIST 2012 Speaker Recognition Evaluation (SRE'12) datasets to validate various points discussed in the paper.

Original languageEnglish
Pages (from-to)3651-3655
Number of pages5
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Publication statusPublished - Aug 2013
Externally publishedYes
Event14th Annual Conference of the International Speech Communication Association, INTERSPEECH 2013 - Lyon, France
Duration: 25 Aug 201329 Aug 2013

Keywords

  • Multi-session training
  • Speaker verification

ASJC Scopus subject areas

  • Language and Linguistics
  • Human-Computer Interaction
  • Signal Processing
  • Software
  • Modelling and Simulation

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