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Factor Analysis and SVM for Language Recognition
Type of publication: Inproceedings
Citation: verd09:interspeech
Booktitle: 10th Annual Conference of the International Speech Communication Association, InterSpeech
Year: 2009
Month: September
Pages: 164-167
Location: Brighton
URL: http://www.hennebert.org/downl...
Abstract: 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 %.
Keywords: Language Identification, Speech Processing
Authors Verdet, Florian
Matrouf, Driss
Bonastre, Jean-François
Hennebert, Jean
Added by: []
Total mark: 0
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