Nonlinear Nuisance Attribute Projection in Combined Kernels for SVM-Based Speaker Verification
Yuan Dong, Liang Lu, Xianyu Zhao, Haila Wang · 2009
This paper investigated the nonlinear nuisance attribute projection (NAP) in combined kernels for SVM-based speaker verification. The combined kernels approach enables the SVM classifier to use several different kinds of kernels together, e.g. linear kernel, RBF kernel, etc, for better classification. To compensate the session variability, which is one of the major reasons for performance degradation, nonlinear kernel NAP was used in this paper to projection out the attribute in the nuisance space which contains mainly the intra speaker variability. Experiments on NIST 2006 SRE corpora shows that, the combined kernels approach outperforms the conventional single kernel SVM approach, while the nonlinear NAP can further enhance this performance gains.