G. Mclachlan and D. Peel, Finite Mixture Models, 2000.
DOI : 10.1002/0471721182

Y. Stylianou, O. Cappé, and E. Moulines, Continuous probabilistic transform for voice conversion, IEEE Transactions on Speech and Audio Processing, vol.6, issue.2, pp.131-142, 1998.
DOI : 10.1109/89.661472

T. Toda, A. W. Black, and K. Tokuda, Voice Conversion Based on Maximum-Likelihood Estimation of Spectral Parameter Trajectory, IEEE Transactions on Audio, Speech and Language Processing, vol.15, issue.8, pp.2222-2235, 2007.
DOI : 10.1109/TASL.2007.907344

H. Zen, Y. Nankaku, and K. Tokuda, Continuous Stochastic Feature Mapping Based on Trajectory HMMs, IEEE Transactions on Audio, Speech, and Language Processing, vol.19, issue.2, pp.417-430, 2011.
DOI : 10.1109/TASL.2010.2049685

Y. Tian, L. Sigal, H. Badino, F. De-la-torre, and Y. Liu, Latent Gaussian Mixture Regression for Human Pose Estimation, ACCV, pp.679-690, 2010.
DOI : 10.1093/biomet/28.3-4.321

URL : http://ca.cs.cmu.edu/projects/lgmr/lgmr.pdf

S. Calinon, F. D-'halluin, E. L. Sauser, D. G. Caldwell, and A. G. Billard, Learning and Reproduction of Gestures by Imitation, IEEE Robotics & Automation Magazine, vol.17, issue.2, pp.44-54, 2010.
DOI : 10.1109/MRA.2010.936947

URL : https://infoscience.epfl.ch/record/147286/files/CalinonEtAl-RAM2010.pdf

T. Hueber, G. Bailly, P. Badin, and F. Elisei, Speaker adaptation of an acousticarticulatory inversion model using cascaded Gaussian mixture regressions, Proceedings of Interspeech, pp.2753-2757, 2013.
DOI : 10.1109/taslp.2015.2464702

J. Gauvain and C. Lee, Maximum a posteriori estimation for multivariate Gaussian mixture observations of Markov chains, IEEE Transactions on Speech and Audio Processing, vol.2, issue.2, pp.291-298, 1994.
DOI : 10.1109/89.279278

M. J. Gales and P. C. Woodland, Mean and variance adaptation within the MLLR framework, Computer Speech & Language, vol.10, issue.4, pp.249-264, 1996.
DOI : 10.1006/csla.1996.0013

URL : http://svr-www.eng.cam.ac.uk/~mjfg/var_CSL.ps.gz

T. Hueber, L. Girin, X. Alameda-pineda, and G. Bailly, Speaker-Adaptive Acoustic-Articulatory Inversion Using Cascaded Gaussian Mixture Regression, IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol.23, issue.12, pp.2246-2259, 2015.
DOI : 10.1109/TASLP.2015.2464702

URL : https://hal.archives-ouvertes.fr/hal-01231197

Z. Ghahramani and M. I. Jordan, Learning from incomplete data, MIT, 1994.
DOI : 10.21236/ADA295618

URL : http://www-cse.ucsd.edu/users/elkan/254/sagarwalrep.pdf

G. Mclachlan and K. Thriyambakam, The EM algorithm and extensions, 1997.

C. M. Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics), 2006.

L. Girin, T. Hueber, and X. Alameda-pineda, Appendix to: Adaptation of a Gaussian mixture regressor to a new input distribution: Extending the C- GMR framework, Tech. Rep, 2016.

L. Ménard, J. Schwartz, L. Boë, and J. Aubin, Articulatory???acoustic relationships during vocal tract growth for French vowels: Analysis of real data and simulations with an articulatory model, Journal of Phonetics, vol.35, issue.1, pp.1-19, 2007.
DOI : 10.1016/j.wocn.2006.01.003

P. Badin and G. Fant, Notes on vocal tract computation, Quarterly Progress and Status Report, pp.53-108, 1984.