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Wiki Cascaded-Gaussian Mixture Regression » Historique » Version 2

Thomas Hueber, 18/10/2016 18:10

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h1. Wiki Cascaded-Gaussian Mixture Regression
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h2. What is C-GMR?
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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) which combines 2 consecutive GMR in a single probabilistic model. For this model, we derived both the exact EM-based training algorithm and inference equation.
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h2. How-to-cite
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T. Hueber, L. Girin, X. Alameda-Pineda, and G. Bailly, « Speaker-adaptive acoustic-articulatory inversion using cascaded Gaussian mixture regression, », IEEE Transactions on Audio, Speech and Language Processing, July 2015, in press.
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h2. Source code
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The Matlab source code for training and using the Integrated C-GMR  and the JointGMR can be downloaded from the C-GMR git repository https://git.gipsa-lab.grenoble-inp.fr/cgmr.git
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h2. Contributors
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* Dr. Thomas Hueber, CNRS researcher, GIPSA-lab (Grenoble, France)
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* Dr. Laurent Girin, Professor at Grenoble-INP, GIPSA-lab/INRIA (Grenoble, France)
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* Dr. Xavier Alameda-Pineda, Researcher at UNITN (Trento, Italy)
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* Dr. Gérard Bailly, CNRS researcher, GIPSA-lab (Grenoble, France)
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h2. Contact
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thomas.hueber@gipsa-lab.fr