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Fast locally linear embedding algorithm for exemplar-based voice conversion

Authors: Yu-Huai Peng; Chin-Cheng Hsu; Yi-Chiao Wu; Hsin-Te Hwang; Yi-Wen Liu; Yu Tsao; Hsin-Min Wang;

Fast locally linear embedding algorithm for exemplar-based voice conversion

Abstract

The locally linear embedding (LLE) algorithm has been proven to have high output quality and applicability for voice conversion (VC) tasks. However, the major shortcoming of the LLE-based VC approach is the time complexity (especially in the matrix inversion process) during the conversion phase. In this paper, we propose a fast version of the LLE algorithm that significantly reduces the complexity. In the proposed method, each locally linear patch on the data manifold is described by a pre-computed cluster of exemplars, and thus the major part of on-line computation can be carried out beforehand in the off-line phase. Experimental results demonstrate that the VC performance of the proposed fast LLE algorithm is comparable to that of the original LLE algorithm and that a real-time VC system becomes possible because of the highly reduced time complexity.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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