A study on GMM-SVM with adaptive relevance factor and its comparison with i-vector and JFA for speaker recognition

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

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

2 Citations (Scopus)

Abstract

Recently, joint factor analysis (JFA) and identity-vector (i-vector) represent the dominant techniques used for speaker recognition due to their superior performance. Developed relatively earlier, the Gaussian mixture model - support vector machine (GMM-SVM) with nuisance attribute projection (NAP) has gradually become less popular. However, when developing the relevance factor in maximum a posteriori (MAP) estimation of GMM to be adapted by application data in place of the conventional fixed value, it is noted that GMM-SVM demonstrates some advantages. In this paper, we conduct a comparative study between GMM-SVM with adaptive relevance factor and JFA/i-vector under the framework of Speaker Recognition Evaluation (SRE) formulated by the National Institute of Standards and Technology (NIST).

Original languageEnglish
Title of host publication2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
Pages7683-7687
Number of pages5
DOIs
Publication statusPublished - 18 Oct 2013
Externally publishedYes
Event2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Vancouver, BC, Canada
Duration: 26 May 201331 May 2013

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
Country/TerritoryCanada
CityVancouver, BC
Period26/05/1331/05/13

Keywords

  • Gaussian mixture model
  • i-vector
  • joint factor analysis
  • maximum a posteriori
  • PLDA
  • support vector machine

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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