Fusion of SNR-dependent PLDA models for noise robust speaker verification

Xiaomin Pang, Man Wai Mak

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

4 Citations (Scopus)

Abstract

The i-vector representation and probabilistic linear discriminant analysis (PLDA) have shown state-of-the-art performance in many speaker verification systems. However, in real-world environments, additive and convolutive noise cause mismatches between training and recognition conditions, degrading the performance. In this paper, a fusion system that combines a multi-condition PLDA model and a mixture of SNR-dependent PLDA models is proposed to make the verification system noise robust. The SNR of test utterances is used to determine the best SNR-dependent PLDA model to score against the target-speaker's i-vectors. The performance of the fusion system is demonstrated on NIST 2012 SRE. Results show that the SNR-dependent PLDA models can reduce EER and that the fusion system is more robust than the conventional i-vector/PLDA systems under noisy conditions. It is also found that the SNR-dependent PLDA models are insensitive to Z-norm parameters.
Original languageEnglish
Title of host publicationProceedings of the 9th International Symposium on Chinese Spoken Language Processing, ISCSLP 2014
PublisherIEEE
Pages619-623
Number of pages5
ISBN (Electronic)9781479942206
DOIs
Publication statusPublished - 24 Oct 2014
Event9th International Symposium on Chinese Spoken Language Processing, ISCSLP 2014 - Singapore, Singapore
Duration: 12 Sep 201414 Sep 2014

Conference

Conference9th International Symposium on Chinese Spoken Language Processing, ISCSLP 2014
CountrySingapore
CitySingapore
Period12/09/1414/09/14

Keywords

  • i-vectors
  • LDA
  • NIST 2012 SRE
  • noise robustness
  • probabilistic
  • Speaker verification

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Software

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