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Support vector machines using GMM supervectors for speaker verification
 IEEE Signal Processing Letters
, 2006
"... pretations, conclusions, and recommendations are those of the authors and are not necessarily endorsed by the United States ..."
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Cited by 116 (5 self)
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pretations, conclusions, and recommendations are those of the authors and are not necessarily endorsed by the United States
SVM based speaker verification using a GMM supervector kernel and NAP variability compensation
 in Proceedings of ICASSP, 2006
"... Gaussian mixture models with universal backgrounds (UBMs) have become the standard method for speaker recognition. Typically, a speaker model is constructed by MAP adaptation of the means of the UBM. A GMM supervector is constructed by stacking the means of the adapted mixture components. A recent d ..."
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Cited by 107 (12 self)
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Gaussian mixture models with universal backgrounds (UBMs) have become the standard method for speaker recognition. Typically, a speaker model is constructed by MAP adaptation of the means of the UBM. A GMM supervector is constructed by stacking the means of the adapted mixture components. A recent discovery is that latent factor analysis of this GMM supervector is an effective method for variability compensation. We consider this GMM supervector in the context of support vector machines. We construct a support vector machine kernel using the GMM supervector. We show similarities based on this kernel between the method of SVM nuisance attribute projection (NAP) and the recent results in latent factor analysis. Experiments on a NIST SRE 2005 corpus demonstrate the effectiveness of the new technique. 1.
SOFT NONNEGATIVE MATRIX COFACTORIZATION WITH APPLICATION TO MULTIMODAL SPEAKER DIARIZATION
"... This paper presents a new method for bimodal nonnegative matrix factorization (NMF). This method is wellsuited to situations where two streams of data are concurrently analyzed and are expected to be related by loosely common factors. It allows for a soft cofactorization, which takes into account ..."
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This paper presents a new method for bimodal nonnegative matrix factorization (NMF). This method is wellsuited to situations where two streams of data are concurrently analyzed and are expected to be related by loosely common factors. It allows for a soft cofactorization, which takes into account the relationship that exists between the modalities being processed, but returns different factors for distinct modalities. There is no need that the data related with each modality live in the same feature space; there is also no need that they have the same dimensionality. The cofactorization is obtained via a majorizationminimization (MM) algorithm. The behavior of the method is illustrated on both synthetic and realworld data. In particular, we show that exploiting the correlation between audio and video modalities in edited talkshow videos improve speaker diarization results. Index Terms—Nonnegative matrix factorization, cofactorization, multimodality, speaker diarization 1.