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ICASSP
2011
IEEE

Speaker recognition using multiple kernel learning based on conditional entropy minimization

12 years 6 months ago
Speaker recognition using multiple kernel learning based on conditional entropy minimization
We applied a multiple kernel learning (MKL) method based on information-theoretic 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 minimization (MCEM). We experimentally veri ed the effectiveness of MCEM for speaker classi cation; this method reduced the speaker error rate as compared to conventional methods.
Tetsuji Ogawa, Hideitsu Hino, Nima Reyhani, Noboru
Added 20 Aug 2011
Updated 20 Aug 2011
Type Journal
Year 2011
Where ICASSP
Authors Tetsuji Ogawa, Hideitsu Hino, Nima Reyhani, Noboru Murata, Tetsunori Kobayashi
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