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

作者：McLaren Mitchell^*; Matrouf Driss; Vogt Robbie; Bonastre Jean Francois

来源：Computer Speech and Language, 2011, 25(2): 327-340.

DOI：10.1016/j.csl.2010.02.004

• Speaker verification
• Factor analysis
• Gaussian mixture model (GMM)
• Support vector machine (SVM)
• Unsupervised adaptation

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.

• 出版日期2011-4