Applying SVMs and weight-based factor analysis to unsupervised adaptation for speaker verification |
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Authors: | Mitchell McLaren Driss Matrouf Robbie Vogt Jean-Francois Bonastre |
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Affiliation: | 1. The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK;2. Center for Neuroscience and Center for Mind and Brain, University of California-Davis, Davis, CA 95618, USA;3. Ernst Strüngmann Institute in Cooperation with Max Planck Society, Deutschordenstraße 46, 60528 Frankfurt, Germany;1. IRAP, CNRS, Université de Toulouse, Observatoire Midi-Pyrénées, 14 Avenue Edouard Belin, F-31400 Toulouse, France;2. Institute of Earth Sciences Jaume Almera, ICTJA-CSIC, Lluis Sole i Sabaris s/n, 08028 Barcelona, Spain |
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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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