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Voice conversion can be formulated as finding a mapping function which transforms the features of a source speaker to those of the target speaker. Gaussian mixture model (GMM)-based conversion techniques [1, 2] have been widely used in voice conversion due to its effectiveness and efficiency. In a recent work [3], we generalized GMM-based mapping to Mixture of Probabilistic Linear Regressions (MPLR)...
We developed a model for the motional impedance Zm of electrostatic resonators that operate in a nonlinear regime. The model predicts that the Zm at resonance has the same expression as it has in the linear case. In addition, we developed a new measurement setup, which is based on a variable-phase feedback circuit. This setup enables the measurement of Zm at operating points that are unobservable...
This paper introduces a model of mixture of probabilistic linear regressions (MPLR) to learn a mapping function between two feature spaces. The MPLR consists of weighted combination of several probabilistic linear regressions, whose parameters are estimated by using matrix calculation. The mixture nature of MPLR allows it to model nonlinear transformation. The formulation of MPLR is general and independent...
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