Adaptive voice-quality control based on one-to-many eigenvoice conversion
نویسندگان
چکیده
This paper presents adaptive voice-quality control methods based on one-to-many eigenvoice conversion. To intuitively control the converted voice quality by manipulating a small number of control parameters, a multiple regression Gaussian mixture model (MR-GMM) has been proposed. The MR-GMM also allows us to estimate the optimum control parameters if target speech samples are available. However, its adaptation performance is limited because the number of control parameters is too small to widely model voice quality of various target speakers. To improve the adaptation performance while keeping capability of voice-quality control, this paper proposes an extendedMR-GMM (EMR-GMM) with additional adaptive parameters to extend a subspace modeling target voice quality. Experimental results demonstrate that the EMR-GMM yields significant improvements of the adaptation performance while allowing us to intuitively control the converted voice quality.
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