Factored covariance modeling for text-independent speaker verification

Eryu Wang, Kong Aik Lee, Bin Ma, Haizhou Li, Wu Guo, Lirong Dai

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

Abstract

Gaussian mixture models (GMMs) are commonly used to model the spectral distribution of speech signals for text-independent speaker verification. Mean vectors of the GMM, used in conjunction with support vector machine (SVM), have shown to be effective in characterizing speaker information. In addition to the mean vectors, covariance matrices capture the correlation between spectral features, which also represent some salient information about speaker identity. This paper investigates the use of local correlation between different dimensions of acoustic vector by using factor analysis and linear Gaussian model. Log-Euclidean inner product kernel is used to measure the similarity between two speech utterances in the form of covariance matrices. Experiments carried on NIST 2006 speaker verification tasks shows promising results.

Original languageEnglish
Title of host publication2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings
Pages4856-4859
Number of pages4
DOIs
Publication statusPublished - Jul 2011
Externally publishedYes
Event36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Prague, Czech Republic
Duration: 22 May 201127 May 2011

Publication series

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

Conference

Conference36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011
Country/TerritoryCzech Republic
CityPrague
Period22/05/1127/05/11

Keywords

  • covariance modeling
  • factor analysis
  • Gaussian mixture model
  • log-Euclidean
  • support vector machine

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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