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Unsupervised adaptation for HMM-based speech synthesis

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IS080299.PDF (176.1Kb)
Date
09/2008
Author
King, Simon
Tokuda, Keiichi
Zen, Heiga
Yamagishi, Junichi
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Abstract
It is now possible to synthesise speech using HMMs with a comparable quality to unit-selection techniques. Generating speech from a model has many potential advantages over concatenating waveforms. The most exciting is model adaptation. It has been shown that supervised speaker adaptation can yield high- quality synthetic voices with an order of magnitude less data than required to train a speaker-dependent model or to build a basic unit-selection system. Such supervised methods require labelled adaptation data for the target speaker. In this paper, we introduce a method capable of unsupervised adaptation, using only speech from the target speaker without any labelling.
URI
http://hdl.handle.net/1842/3841
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  • CSTR publications
  • Linguistics and English Language publications

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