This paper addresses the problem of the adaptation of a Gaussian Mixture Regression (MGR) 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.
CITATION STYLE
Girin, L., Hueber, T., & Alameda-Pineda, X. (2017). Adaptation of a gaussian mixture regressor to a new input distribution: Extending the C-GMR framework. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10169 LNCS, pp. 459–468). Springer Verlag. https://doi.org/10.1007/978-3-319-53547-0_43
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