Applying SVMs and weight-based factor analysis to unsupervised adaptation for speaker verification

Abstract : This paper presents an extended study on the implementation of support vector machine (SVM) based speaker verification in systems that employ continuous progressive model adaptation using the weight-based factor analysis model. The weight-based factor analysis model compensates for session variations in unsupervised scenarios by incorporating trial confidence measures in the general statistics used in the inter-session variability modelling process. Employing weight-based factor analysis in Gaussian mixture models (GMMs) was recently found to provide significant performance gains to unsupervised classification. Further improvements in performance were found through the integration of SVM-based classification in the system by means of GMM supervectors. This study focuses particularly on the way in which a client is represented in the SVM kernel space using single and multiple target supervectors. Experimental results indicate that training client SVMs using a single target supervector maximises performance while exhibiting a certain robustness to the inclusion of impostor training data in the model. Furthermore, the inclusion of low-scoring target trials in the adaptation process is investigated where they were found to significantly aid performance.
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https://hal.archives-ouvertes.fr/hal-01318530
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Submitted on : Thursday, May 19, 2016 - 4:27:05 PM
Last modification on : Tuesday, July 2, 2019 - 5:38:02 PM

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Mitchell Mclaren, Driss Matrouf, Robbie Vogt, Jean-François Bonastre. Applying SVMs and weight-based factor analysis to unsupervised adaptation for speaker verification. Computer Speech and Language, Elsevier, 2011, ⟨10.1016/j.csl.2010.02.004⟩. ⟨hal-01318530⟩

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