ISCA Archive Interspeech 2009
ISCA Archive Interspeech 2009

Factor analysis and SVM for language recognition

Florian Verdet, Driss Matrouf, Jean-François Bonastre, Jean Hennebert

Statistic classifiers operate on features that generally include both, useful and useless information. These two types of information are difficult to separate in feature domain. Recently, a new paradigm based on Factor Analysis (FA) proposed a model decomposition into useful and useless components. This method has successfully been applied to speaker recognition tasks. In this paper, we study the use of FA for language recognition. We propose a classification method based on SDC features and Gaussian Mixture Models (GMM). We present well performing systems using Factor Analysis and FA-based Support Vector Machine (SVM) classifiers. Experiments are conducted using NIST LRE 2005’s primary condition. The relative equal error rate reduction obtained by the best factor analysis configuration with respect to baseline GMM-UBM system is over 60%, corresponding to an EER of 6.59%.

doi: 10.21437/Interspeech.2009-66

Cite as: Verdet, F., Matrouf, D., Bonastre, J.-F., Hennebert, J. (2009) Factor analysis and SVM for language recognition. Proc. Interspeech 2009, 164-167, doi: 10.21437/Interspeech.2009-66

  author={Florian Verdet and Driss Matrouf and Jean-François Bonastre and Jean Hennebert},
  title={{Factor analysis and SVM for language recognition}},
  booktitle={Proc. Interspeech 2009},