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

Authors:
Mitchell McLaren;Driss Matrouf;Robbie Vogt;Jean-Francois Bonastre
Affiliations:
Laboratoire Informatique D'Avignon (LIA), Universite d'Avignon, Agroparc BP 1228, 84911 Avignon Cedex 9, France and Speech and Audio Research Laboratory, Queensland University of Technology (QUT), ...;Laboratoire Informatique D'Avignon (LIA), Universite d'Avignon, Agroparc BP 1228, 84911 Avignon Cedex 9, France;Speech and Audio Research Laboratory, Queensland University of Technology (QUT), GPO Box 2434, Brisbane 4001, Australia;Laboratoire Informatique D'Avignon (LIA), Universite d'Avignon, Agroparc BP 1228, 84911 Avignon Cedex 9, France
Venue:
Computer Speech and Language
Year:
2011

Citing 3
Cited 1

A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery
Data-driven background dataset selection for SVM-based speaker verification

IEEE Transactions on Audio, Speech, and Language Processing
A Joint Factor Analysis Approach to Progressive Model Adaptation in Text-Independent Speaker Verification

IEEE Transactions on Audio, Speech, and Language Processing

Exploring similarity-based classification of larynx disorders from human voice

Speech Communication

Quantified Score

Hi-index	0.01

Visualization

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.