Abstract
Almost of the current spectral conversion methods required parallel corpus containing the same utterances from source and target speakers, which was often inconvenient and sometimes hard to fulfill. This paper proposed a novel algorithm for text-independent voice conversion, which can relax the parallel constraint. The proposed algorithm was based on speaker adaptation technique of kernel eigenvoice, adapting the conversion parameters derived for the pre-stored pairs of speakers to a desired pair, for which only a nonparallel corpus was available. Objective evaluation results demonstrated that the proposed kernel eigenvoice algorithm can effectively improve converted spectral similarity in a text-independent manner.
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Li, Y., Zhang, L., Ding, H. (2010). Text-Independent Voice Conversion Based on Kernel Eigenvoice. In: Wang, F.L., Deng, H., Gao, Y., Lei, J. (eds) Artificial Intelligence and Computational Intelligence. AICI 2010. Lecture Notes in Computer Science(), vol 6319. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16530-6_51
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DOI: https://doi.org/10.1007/978-3-642-16530-6_51
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16529-0
Online ISBN: 978-3-642-16530-6
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