2009 15th Asia-Pacific Conference on Communications 2009
DOI: 10.1109/apcc.2009.5375644
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A global covariance matrix based principal component analysis for speaker identification

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“…as Gaussian Mixture Models (GMM)s can be fitted assuming diagonal covariance matrices (Rao and Koolagudi, 2013). This method of defining global principal components was proposed for dimensionality reduction in speaker identification using the GMM-UBM system in (Seo et al, 2009). It was also found to outperform concatenation of features by Sarkar et al (2014).…”
Section: Introductionmentioning
confidence: 99%
“…as Gaussian Mixture Models (GMM)s can be fitted assuming diagonal covariance matrices (Rao and Koolagudi, 2013). This method of defining global principal components was proposed for dimensionality reduction in speaker identification using the GMM-UBM system in (Seo et al, 2009). It was also found to outperform concatenation of features by Sarkar et al (2014).…”
Section: Introductionmentioning
confidence: 99%