A fast speaker verification with universal background support data selection
Gang Liu, Jun-Won Suh, John H. L. Hansen · 2012
In this study, a fast universal background support imposter data selection method is proposed, which is integrated within a support vector machine (SVM) based speaker verification system. Selection of an informative background dataset is crucial in constructing a discriminative decision super-plane between the enrollment and imposter speakers. Previous studies generally derive the optimal number of imposter examples from development data and apply to the evaluation data, which cannot guarantee consistent performance and often necessitate expensive searching. In the proposed method, the universal background dataset is derived so as to embed imposter knowledge in a more balanced way. Next, the derived dataset is taken as the imposter set in the SVM modeling process for each enrollment speaker. By using imposter adaptation, a more detailed subspace per target speaker can be constructed. Compared to the popular support-vector frequency based method, the proposed method can not only avoid parameter searching but offers a significant improvement and generalizes better on the unseen data.