Kernel eigenvoice

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Speaker adaptation is an important technology to fine-tune either features or speech models for mis-match due to inter-speaker variation. In the last decade, eigenvoice (EV) speaker adaptation has been developed. It makes use of the prior knowledge of training speakers to provide a fast adaptation algorithm (in other words, only a small amount of adaptation data is needed). Inspired by the kernel eigenface idea in face recognition, kernel eigenvoice (KEV) is proposed.{{Cite web |url=http://www.cs.ust.hk/~mak/PG-Thesis/thesis-simon.pdf |title=Kernel Eigenvoice Thesis |access-date=2009-07-17 |archive-url=https://web.archive.org/web/20110610141128/http://www.cs.ust.hk/~mak/PG-Thesis/thesis-simon.pdf |archive-date=2011-06-10 |url-status=dead }} KEV is a non-linear generalization to EV. This incorporates Kernel principal component analysis, a non-linear version of Principal Component Analysis, to capture higher order correlations in order to further explore the speaker space and enhance recognition performance.

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