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

Laurent Girin 1, 2, * Thomas Hueber 1 Xavier Alameda-Pineda 3, 2
* Corresponding author
1 GIPSA-CRISSP - CRISSP
GIPSA-DPC - Département Parole et Cognition
2 PERCEPTION - Interpretation and Modelling of Images and Videos
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
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.
Document type :
Conference papers
Complete list of metadatas

Cited literature [16 references]  Display  Hide  Download

https://hal.inria.fr/hal-01646098
Contributor : Xavier Alameda-Pineda <>
Submitted on : Thursday, November 23, 2017 - 11:33:34 AM
Last modification on : Thursday, February 7, 2019 - 3:47:12 PM

File

Girin-LVA-2017.pdf
Files produced by the author(s)

Identifiers

Citation

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

Share

Metrics

Record views

546

Files downloads

129