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Generalized Variability Model for Speaker Verification
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DOI：10.1109/LSP.2018.2874814.png)
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Abstract
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In this letter, we propose a generalized variability model as an extension to the total variability model. While the total variability model employs a standard normal prior distribution in its typical setup, the proposed generalized variability model relaxes this assumption and allows the latent variable distribution to be a mixture of Gaussians. The conventional total variability model can then be viewed as a special case of this generalized version where the number of mixture components is constrained to one. This proposed model is validated in the context of speaker verification tasks on both the standard and extended NIST SRE 2010 datasets. Experimental results show that modeling the distribution of the latent variables as a mixture of Gaussians leads to a better performance under all conditions and a greater gain can be expected for speaker verification using short utterances.
Keywords:
Total variability model
generalized variability model
i-vector
standard normal prior
mixture of Gaussian prior
speaker verification
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