SVM-based Speaker Classification in the GMM Models Space
Odyssey（2006）
摘要
This paper describes a new approach to speaker classification, based on using an SVM classifier over the GMM models space. Adaptation of a speaker-independent GMM universal background model with speaker specific data creates a speaker-dependent GMM model. The vector representation of this model is used by an SVM classifier to recognize the speaker. When used with multiple, channel-specific background models, this scheme has the potential to improve speaker recognition performance in channel mismatch conditions. Performance improvement is demonstrated over a multi-channel corpus, as well as over the NIST 2004 evaluation data
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关键词
gaussian distribution,signal classification,signal representation,speaker recognition,support vector machines,gmm,gaussian mixture model,nist 2004 evaluation data,svm-based speaker classification,channel mismatch condition,multichannel corpus,support vector machine,universal background model,vector representation,polynomials,kernel,nist,cepstrum,hilbert space,testing
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