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Fast locally linear embedding algorithm for exemplar-based voice conversion
Conference paper

Fast locally linear embedding algorithm for exemplar-based voice conversion

Yu-Huai Peng, Chin-Cheng Hsu, Yi-Chiao Wu, Hsin-Te Hwang, Yi-Wen Liu, Yu Tsao and Hsin-Min Wang
Proceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017, Vol.2018-February, pp.591-595
02/2018

Abstract

Artificial Intelligence Human-Computer Interaction Information Systems Signal Processing
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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