Wiki Cascaded-Gaussian Mixture Regression¶

What is C-GMR?¶

Cascaded Gaussian Mixture Regression or C-GMR is a general framework for adapting a GMR (Gaussian Mixture Regression) trained on a large dataset of input-output joint observations, using a limited set of input-only observations. It was originaly developed in the context of speech processing fpr adapting an acoustic-articulatory inversion GMM trained on a reference speaker, to a new speaker, given a small amount of audio-only observations. In particular, C-GMR framework includes the "Integrated C-GMR" model (IC-GMR) and the Joint-GMR which combine 2 consecutive GMR in a single probabilistic model. For these models, we derived both the exact EM-based training algorithm and inference equation.

How-to-cite¶

Hueber, T., Girin, L., Alameda-Pineda, X., Bailly, G. (2015), "Speaker-Adaptive Acoustic-Articulatory Inversion using Cascaded Gaussian Mixture Regression", in Audio, Speech, and Language Processing, IEEE/ACM Transactions on, vol. 23, no. 12, pp. 2246-2259

Source code¶

The Matlab source code for training and using the Integrated C-GMR and the Joint-GMR can be downloaded from the C-GMR git repository https://git.gipsa-lab.grenoble-inp.fr/cgmr.git

Contributors¶

• Dr. Thomas Hueber, CNRS researcher, GIPSA-lab (Grenoble, France)
• Dr. Laurent Girin, Professor at Grenoble-INP, GIPSA-lab/INRIA (Grenoble, France)
• Dr. Xavier Alameda-Pineda, Researcher at UNITN (Trento, Italy)
• Dr. Gérard Bailly, CNRS researcher, GIPSA-lab (Grenoble, France)

Contact¶

thomas.hueber@gipsa-lab.fr