Support Vector Classifier with Linguistic Interpretation of the Kernel Matrix in Speaker Verification
The paper shows that support vector classifier with linguistic interpretation of the kernel matrix can be effectively used in speaker verification. The kernel matrix is obtained by means of fuzzy clustering, based on global learning of fuzzy system with logical interpretation of if-then rules and with parametric conclusions. The kernel matrix is data-dependent and may be interpreted in terms of linguistic values related to the premises of if-then rules. Simulation results obtained for SPIDRE corpus are presented for comparison with traditional methods used in speaker verification.
Keywordssupport vector machine kernel matrix fuzzy system speaker verification
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