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Therefore, we compare VMR with an existing VC system (BOINC).
The map stage was up to 4 times faster than in an existing VC system.
The VC system is comprised of two phases: training and transformation.
The proposed VC system depicted in Figure2, consists of two phases: training and transformation.
The VC system needs to realize the transformation model considering the speaker-specific features[22].
A voice conversion (VC) system was designed based on Gaussian mixture model (GMM) and radial basis function (RBF) neural network.
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Furthermore, it is compatible with existing VC systems (in particular BOINC).
Fault tolerance is strictly confined to the client-side in current VC systems.
Fault tolerance is mostly confined to the client-side in current VC systems.
In our approach, the 'symmetrical optimum' rule was applied for the first time to VC systems.
As a consequence, the design of such VC systems is solely based on the time constants of the clamp circuit.
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