SPEAKER RECOGNITION USING MULTIPLE KERNEL LEARNING BASED ON
Hideitsu Hino, Nima Reyhani, Noboru Murata, Tetsunori Kobayashi · 2011
We applied a multiple kernel learning (MKL) method based on information-theo retic optimization to speaker recognition. Most of the kernel methods applied to speaker recognition systems require a suitable kernel function and its parameters to be determined for a given data set. In contrast, MKL eliminates the need for strict determination of the kernel function and parameters by using a convex combination of element kernels. In the present paper, we describe an MKL algorithm based on conditional entropy mini mization (MCEM). We experimentally verified the effectiveness of MCEM for speaker classification; this method reduced the speaker error rate as compared to conventional methods.