Multiple kernel learning for speaker verification

Many speaker verification (SV) systems combine multiple classifiers using score-fusion to improve system performance. For SVM classifiers, an alternative strategy is to combine at the kernel level. This involves finding a suitable kernel weighting, known as multiple kernel learning (MKL). Recently, an efficient maximum-margin scheme for MKL has been proposed. This work examines several refinements to this scheme for SV. The standard scheme has a known tendency towards sparse weightings, which may not be optimal for SV. A regularisation term is proposed, allowing the appropriate level of sparsity to be selected. Cross-speaker tying of kernel weights is also applied to improve robustness. Various combinations of dynamic kernels were evaluated, including derivative and parametric kernels based upon different model structures. The performance achieved on the NIST 2002 SRE when combining five kernels was 4.83% EER.