A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Improved SVM speaker verification through data-driven background dataset collection
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Speaker Verification Using Support Vector Machines and High-Level Features
IEEE Transactions on Audio, Speech, and Language Processing
Speaker Recognition With Session Variability Normalization Based on MLLR Adaptation Transforms
IEEE Transactions on Audio, Speech, and Language Processing
SVM speaker verification using session variability modelling and GMM supervectors
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Applying SVMs and weight-based factor analysis to unsupervised adaptation for speaker verification
Computer Speech and Language
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The recently proposed data-driven background dataset refinement technique provides a means of selecting an informative background for support vector machine (SVM)-based speaker verification systems. This paper investigates the characteristics of the impostor examples in such highly informative background datasets. Data-driven dataset refinement individually evaluates the suitability of candidate impostor examples for the SVM background prior to selecting the highest-ranking examples as a refined background dataset. Further, the characteristics of the refined dataset were analyzed to investigate the desired traits of an informative SVM background. The most informative examples of the refined dataset were found to consist of large amounts of active speech and distinctive language characteristics. The data-driven refinement technique was shown to filter the set of candidate impostor examples to produce a more disperse representation of the impostor population in the SVM kernel space, thereby reducing the number of redundant and less-informative examples in the background dataset. Furthermore, data-driven refinement was shown to provide performance gains when applied to the difficult task of refining a small candidate dataset that was mismatched to the evaluation conditions.