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Speaker de-identification approaches must accomplish three main goals: universality, naturalness and reversibility. The main drawback of the traditional approach to speaker de-identification using voice conversion techniques is its lack of universality, since a parallel corpus between the input and target speakers is necessary to train the conversion parameters. It is possible to make use of a synthetic...
The main drawback of speaker de-identification approaches using voice conversion techniques is the need for parallel corpora to train transformation functions between the source and target speakers. In this paper, a voice conversion approach that does not require training any parameters is proposed: it consists in manually defining frequency warping (FW) based transformations by using piecewise linear...
This paper presents a new method for cross-lingual speaker adaptation in the framework of HMM-based speech synthesis. Taking two HTS voice models as input, one for the desired language and another for the aimed speaker identity, it yields a third model that produces speech in the target language while sounding like the target speaker. The method operates at segmental level (spectral information and...
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