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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)...
This paper proposes hidden structure model (HSM) for statistical modeling of sequence data. The HSM generalizes our previous proposal on structural representation by introducing hidden states and probabilistic models. Compared with the previous structural representation, HSM not only can solve the problem of misalignment of events, but also can conduct structure-based decoding, which allows us to...
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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