Relevance vector machines with empirical likelihood-ratio kernels for PLDA speaker verification

Wei Rao, Man Wai Mak

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

1 Citation (Scopus)

Abstract

Previous works have shown the benefits of empirical likelihood ratio (LR) kernels for i-vector/PLDA speaker verification. The method not only utilizes the multiple enrollment utterances of target speakers effectively, but also opens up opportunity for adopting sparse kernel machines for PLDA-based speaker verification systems. This paper proposes taking the advantages of the empirical LR kernels by incorporating them into relevance vector machines (RVMs). Results on NIST 2012 SRE demonstrate that the performance of RVM regression equipped with empirical LR kernels is slightly better than that of the support vector machines after performing utterance partitioning.
Original languageEnglish
Title of host publicationProceedings of the 9th International Symposium on Chinese Spoken Language Processing, ISCSLP 2014
PublisherIEEE
Pages64-68
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

  • Empirical LR kernel
  • I-vectors
  • NIST SRE
  • Probabilistic linear discriminant analysis
  • Relevance vector machines

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Software

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