| HAL-Inria | Publications, software ... of Inria's scientists |
Conference papers
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 Connect in order to contact the contributor
Submitted on : Thursday, November 23, 2017 - 11:33:34 AM
Last modification on : Tuesday, October 19, 2021 - 11:27:14 AM
Contributor : Xavier Alameda-Pineda Connect in order to contact the contributor
Submitted on : Thursday, November 23, 2017 - 11:33:34 AM
Last modification on : Tuesday, October 19, 2021 - 11:27:14 AM
File
Girin-LVA-2017.pdf
Files produced by the author(s)