Reda Dehak, Najim Dehak, Patrick Kenny, Pierre Dumouchel.
Kernel Combination for SVM Speaker Verification. In the proceedings of
The Speaker and Language Recognition Workshop (Odyssey 2008) Stellenbosch, South Africa January 21-24, 2008
We present a new approach for constructing the kernels used to build
support vector machines for speaker verification. The idea is to
construct new kernels by taking linear combination of many kernels
such as the GLDS and GMM supervector kernels. In this new kernel
combination, the combination weights are speaker dependent rather
than universal weights on score level fusion and there is no need
for extra-data to estimate them. An experiment on the NIST 2006
speaker recognition evaluation dataset (all trial) was done using
three different kernel functions (GLDS kernel, linear and Gaussian
GMM supervector kernels). We compared our kernel combination to the
optimal linear score fusion obtained using logistic regression. This
optimal score fusion was trained on the same test data. We had an
equal error rate of $\simeq 5,9\%$ using the kernel combination
technique which is better than the optimal score fusion system
($\simeq 6,0\%$).
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