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Nuisance attribute projection (NAP) was successfully applied in SVM-based speaker verification systems to improve performance by doing projection to remove dimensions from the SVM feature space that cause unwanted variability in the kernel. Previous studies of NAP were focused mainly on linear and generalized linear kernel SVMs. In this paper, NAP in nonlinear kernel SVMs, e.g. polynomial or Gaussian...
In this paper, we investigate SVM-based speaker verification by location in the space of reference speakers. Speaker location is represented by a vector of log-likelihoods of utterance data given reference speaker models. Channel or session variability in speaker locations due to microphone, acoustic environments etc. would impair verification performance. To reduce such variability, Within-Class...
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