Wiki Cascaded-Gaussian Mixture Regression » Historique » Révision 3
Révision 2 (Thomas Hueber, 18/10/2016 18:10) → Révision 3/12 (Thomas Hueber, 18/10/2016 18:18)
h1. Wiki Cascaded-Gaussian Mixture Regression h2. 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 combines 2 consecutive GMR in a single probabilistic model. For these models, this model, we derived both the exact EM-based training algorithm and inference equation. h2. How-to-cite T. Hueber, T., L. Girin, L., X. Alameda-Pineda, X., and G. Bailly, G. (2015), "Speaker-Adaptive Acoustic-Articulatory Inversion « Speaker-adaptive acoustic-articulatory inversion using Cascaded cascaded Gaussian Mixture Regression", in mixture regression, », IEEE Transactions on Audio, Speech, Speech and Language Processing, IEEE/ACM Transactions on, vol. 23, no. 12, pp. 2246-2259 July 2015, in press. h2. Source code The Matlab source code for training and using the Integrated C-GMR and the Joint-GMR JointGMR can be downloaded from the C-GMR git repository https://git.gipsa-lab.grenoble-inp.fr/cgmr.git h2. 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) h2. Contact thomas.hueber@gipsa-lab.fr