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Sparse nonlinear representation for voice conversion
2015
2015 IEEE International Conference on Multimedia and Expo (ICME)
In voice conversion, sparse-representation-based methods have recently been garnering attention because they are, relatively speaking, not affected by over-fitting or over-smoothing problems. In these approaches, voice conversion is achieved by estimating a sparse vector that determines which dictionaries of the target speaker should be used, calculated from the matching of the input vector and dictionaries of the source speaker. The sparse-representation-based voice conversion methods can be
doi:10.1109/icme.2015.7177437
dblp:conf/icmcs/NakashikaTA15
fatcat:r5bn6swx4bgnjeoc5arofxsnky