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.