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Adaptation of a Gaussian Mixture Regressor to a New Input Distribution: Extending the C-GMR Framework
Abstract : This paper addresses the problem of the adaptation of a Gaussian Mixture Regression (GMR) to a new input distribution, using a limited amount of input-only examples. We propose a new model for GMR adaptation, called Joint GMR (J-GMR), that extends the previously published framework of Cascaded GMR (C-GMR). We provide an exact EM training algorithm for the J-GMR. We discuss the merits of the J-GMR with respect to the C-GMR and illustrate its performance with experiments on speech acoustic-to-articulatory inversion.
Cited literature [16 references]
https://hal.inria.fr/hal-01646098
Contributor : Xavier Alameda-Pineda <>
Submitted on : Thursday, November 23, 2017 - 11:33:34 AM
Last modification on : Wednesday, October 7, 2020 - 11:24:19 AM
Contributor : Xavier Alameda-Pineda <>
Submitted on : Thursday, November 23, 2017 - 11:33:34 AM
Last modification on : Wednesday, October 7, 2020 - 11:24:19 AM
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Girin-LVA-2017.pdf
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