SNR-dependent mixture of PLDA for noise robust speaker verification

Research output: Journal article publicationConference articleAcademic researchpeer-review

9 Citations (Scopus)


This paper proposes a mixture of SNR-dependent PLDA models to provide a wider coverage on the i-vector spaces so that the resulting i-vector/PLDA system can handle test utterances with a wide range of SNR. To maximise the coordination among the PLDA models, they are trained simultaneously via an EM algorithm using utterances contaminated with noise at various levels. The contribution of a training i-vector to individual PLDA models is determined by the posterior probability of the utterance's SNR. Given a test i-vector, the marginal likelihoods from individual PLDA models are linear combined based on the the posterior probabilities of the test utterance and the targetspeaker's utterance. Verification scores are the ratio of the marginal likelihoods. Results based on NIST 2012 SRE suggest that this soft-decision scheme is particularly suitable for the situations where the test utterances exhibit a wide range of SNR.
Original languageEnglish
Pages (from-to)1855-1859
Number of pages5
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Publication statusPublished - 1 Jan 2014
Event15th Annual Conference of the International Speech Communication Association: Celebrating the Diversity of Spoken Languages, INTERSPEECH 2014 - Max Atria at Singapore Expo, Singapore, Singapore
Duration: 14 Sept 201418 Sept 2014


  • I-vectors
  • Mixture of plda
  • Noise robustness
  • Probabilistic lda
  • Speaker verification

ASJC Scopus subject areas

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


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