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Embedded kernel eigenvoice speaker adaptation and its implication to reference speaker weighting
IEEE Transactions on Audio, Speech, and Language Processing
Recently, we proposed an improvement to the conventional eigenvoice (EV) speaker adaptation using kernel methods. In our novel kernel eigenvoice (KEV) speaker adaptation, speaker supervectors are mapped to a kernel-induced high dimensional feature space, where eigenvoices are computed using kernel principal component analysis. A new speaker model is then constructed as a linear combination of the leading eigenvoices in the kernel-induced feature space. KEV adaptation was shown to outperform EV,doi:10.1109/tsa.2005.860836 fatcat:wwpinoarrfdbxlgvelkydha36q