Bhattacharyya-based GMM-SVM system with adaptive relevance factor for pair language recognition

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

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

1 Citation (Scopus)


In this paper, we develop a hybrid system for pair language recognition using Gaussian mixture model (GMM) supervector connecting to support vector machine (SVM). The adaptation of relevance factor in maximum a posteriori (MAP) adaptation of GMM from universal background model (UBM) is studied. In conventional MAP, relevance factor is empirically given by a constant value. It has been proven that the relevance factor can be dependent to the particular application. We use the relevance factor to control the degree of influence from the observed training data for more effectiveness. In order to design a robust pair language recognition system, we develop a hybrid scheme by using separate-training Bhattacharyya-based kernels with the adaptive relevance factor applied. The pair language recognition system is verified on National Institute of Standards and Technology (NIST) language recognition evaluation (LRE) 2011 task. Experiments show the improvement of the performance brought by the proposed scheme.

Original languageEnglish
Title of host publicationOdyssey 2012 - Speaker and Language Recognition Workshop
EditorsHaizhou Li, Bin Ma, Kong Aik Lee
PublisherChinese and Oriental Languages Information Processing Society (COLIPS), Speaker and Language Characterization SIG
Number of pages8
ISBN (Electronic)9789810730932
Publication statusPublished - Jun 2012
Externally publishedYes
EventSpeaker and Language Recognition Workshop, Odyssey 2012 - Singapore, Singapore
Duration: 25 Jun 201228 Jun 2012

Publication series

NameOdyssey 2012 - Speaker and Language Recognition Workshop


ConferenceSpeaker and Language Recognition Workshop, Odyssey 2012


  • Gaussian mixture model
  • Maximum a posteriori
  • Supervector
  • Support vector machine

ASJC Scopus subject areas

  • Signal Processing
  • Software
  • Human-Computer Interaction


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