SNR-invariant PLDA modeling for robust speaker verification

Research output: Journal article publicationConference articleAcademic researchpeer-review

7 Citations (Scopus)


In spite of the great success of the i-vector/PLDA framework, speaker verification in noisy environments remains a challenge. To compensate for the variability of i-vectors caused by different levels of background noise, this paper proposes a new framework, namely SNR-invariant PLDA, for robust speaker verification. By assuming that i-vectors extracted from utterances falling within a narrow SNR range share similar SNRspecific information, the paper introduces an SNR factor to the conventional PLDA model. Then, the SNR-related variability and the speaker-related variability embedded in the i-vectors are modeled by the SNR factor and the speaker factor, respectively. Accordingly, an i-vector is represented by a linear combination of three components: speaker, SNR, and channel. During verification, the variability due to SNR and channels are marginalized out when computing the marginal likelihood ratio. Experiments based on NIST 2012 SRE show that SNR-invariant PLDA achieves superior performance when compared with the conventional PLDA and SNR-dependent mixture of PLDA.
Original languageEnglish
Pages (from-to)2317-2321
Number of pages5
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Publication statusPublished - 1 Jan 2015
Event16th Annual Conference of the International Speech Communication Association, INTERSPEECH 2015 - International Congress Center, Dresden, Germany
Duration: 6 Sept 201510 Sept 2015


  • I-vector
  • PLDA
  • SNR-invariant
  • Speaker verification

ASJC Scopus subject areas

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


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