2009
DOI: 10.1007/s00521-009-0275-x
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Unaligned training for voice conversion based on a local nonlinear principal component analysis approach

Abstract: During the past years, various principal component analysis algorithms have been developed. In this paper, a new approach for local nonlinear principal component analysis is proposed which is applied to capture voice conversion (VC). A new structure of autoassociative neural network is designed which not only performs data partitioning but also extracts nonlinear principal components of the clusters. Performance of the proposed method is evaluated by means of two experiments that illustrate its efficiency; at … Show more

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Cited by 11 publications
(12 citation statements)
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“…The proposed algorithm has been shown to be efficient in complex non-convex optimization problems dealing with local minima issues, although, depending on the number of optimization parameters, may be slow in convergence [42]. Finally, note that, with proper modifications, the proposed algorithm can be used for other optimization problems as well.…”
Section: ) Short-term Power Constraintmentioning
confidence: 96%
“…The proposed algorithm has been shown to be efficient in complex non-convex optimization problems dealing with local minima issues, although, depending on the number of optimization parameters, may be slow in convergence [42]. Finally, note that, with proper modifications, the proposed algorithm can be used for other optimization problems as well.…”
Section: ) Short-term Power Constraintmentioning
confidence: 96%
“…Furthermore, our experiments show that the algorithm is much more efficient than using greedy search scheme which requires a large number of initial random seeds due to the non-convexity of (13). Finally, although it may be time-consuming when the number of optimization parameters increases, the proposed algorithm has been shown to be efficient in many complex optimization problems dealing with local minima issues [42].…”
Section: Average Rate With No Csi At the Transmittermentioning
confidence: 84%
“…Furthermore, our experiments show that the algorithm is much more efficient than using a greedy search scheme which requires a large number of initial random seeds due to the nonconvexity of (23). Finally, although it may be time consuming when the number of optimization parameters increases, the proposed algorithm has been shown to be efficient in many complex optimization problems dealing with local minima issues [50].…”
Section: Long-term Fairness Constraintmentioning
confidence: 85%