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Adaptation of a Gaussian Mixture Regressor to a New Input Distribution: Extending the C-GMR Framework

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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.
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Dates and versions

hal-01646098 , version 1 (23-11-2017)

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Laurent Girin, Thomas Hueber, Xavier Alameda-Pineda. Adaptation of a Gaussian Mixture Regressor to a New Input Distribution: Extending the C-GMR Framework. LVA/ICA 2017 - 13th International Conference on Latent Variable Analysis and Signal Separation, Feb 2017, Grenoble, France. pp.459-468, ⟨10.1007/978-3-319-53547-0_43⟩. ⟨hal-01646098⟩
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