Gaussian mixture models (GMMs) are widely used in data mining, pattern recognition, machine learning, and statistical analysis; where, usually, their parameters are determined by maximum likelihood and the EM algorithm. The key idea is to model the joint probability density function of the data (inputs and ouputs included) using a Gaussian mixture model. Then we obtain the regression value by using the concept of conditional expectation (see enclosed PDF file).
These scripts require NETLAB toolbox for Matlab.
In case you use the code above you could cite this article: Alexander Sepúlveda, Rodrigo Capobianco Guido, and G. Castellanos-Dominguez. 2013. Estimation of relevant time-frequency features using Kendall coefficient for articulator position inference. Speech Commun. 55, 1 (January, 2013), 99–110. DOI:https://doi.org/10.1016/j.specom.2012.06.005